Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial Power Rated Ripple Power Compensation Strategies for $1-\phi$ Bidirectional AC-DC Matrix Converters

Compensation of ripple power required during the $\mathbf{1}-\phi$ operation of direct matrix converters needs to be addressed in order to improve their power density, reliability, cost effectiveness, etc. Literature survey reveals that compensation strategies relevant to matrix converters are usually implemented either at the low frequency AC side of the matrix converter or at the DC port side. In such cases, the entire ripple power needs to be handled by the compensation block, thereby effecting metrics such as voltage or current stresses, losses, power density, etc. In view of this, two circuits are described in this paper that perform active ripple power compensation of direct matrix converter based isolated bidirectional AC-DC converters while handling a fraction of the actual ripple power. The compensation circuits are operated at the high frequency link of the AC-DC converter on the DC port side. Relevant explanation of the working principle of the topologies are given, and simulation results for a 3.45 $\mathbf{kW}$ electric vehicle charger, with a 230 V (RMS) AC grid input, feeding a 400 V battery are presented to illustrate the findings.
Output Frequency Calculation for VCO Based on Nonlinear State Space Model

This paper delves into an exploration of the dynamic behavior and control complexities surrounding Voltage-Controlled Oscillators (VCOs) within the domain of high-speed data transmission systems. The study presents an exhaustive analysis of the intricate dynamics inherent to VCOs, accounting for their inherent nonlinearity. To capture these nonlinearity and dynamic traits with precision, we meticulously construct a comprehensive state-space model. Our methodology offers a nuanced comprehension of VCO dynamics, surpassing the scope of conventional linear models. Through a thorough investigation of the model’s intricacies and attributes, we illustrate its potential to surmount the limitations of linear counterparts. Additionally, we discuss the implications of this nonlinear state-space model in shaping robust and efficient controllers, thereby opening avenues for future research. The application of these advancements holds the potential to significantly elevate VCO performance in high-speed data transmission applications.
A FPGA-Based Iterative 6DoF Pose Refinement Processing Unit for Fast and Energy-Efficient Pose Estimation in Picking Robots

Fast and energy-efficient 6D pose estimation is essential for robotic applications, especially for picking robots in industrial scene. Introducing iterative pose refinement in the final stage of pose estimation pipeline can effectively improve the precision. However, this procedure can be very time consuming due to iterative convolutional neural network (CNN) inference on resource and power constrained platforms. In this paper, we propose a FPGA-based iterative pose refinement processing unit that achieves fast and energy-efficient pose estimation for picking robots. The design and implementation are based on a Xilinx Zynq UltraScale+ MPSoC. Our experimental results demonstrate that the proposed FPGA-Based processing unit is 21.78 times faster and 23.89 times more energy-efficient compared with the baseline, significantly enhances the speed and energy efficiency of pose refinement. The evaluation result on the datasets shows little accuracy drop compared to the baseline implementation.
A Hot Redundancy Method For Virtualized Industrial Edge Applications

With the rapid evolution of industrial architecture, industrial edge computing is one of the most popular architectures at present. However, backup methods designed for traditional industrial architectures are not satisfactory in terms of device cost and applicability when they are applied in this new architecture. Against this background, this work presents a new backup method for industrial edge applications with better performance on resource consumption and high stability. The Function Block of IEC 61499 is used as the standard industrial edge application in this work. By carrying out experiments and employing multiple backup approaches, the resource occupancy rate and recovery time can be reduced simultaneously. The innovative methods and resource allocation models tackle the limitation of traditional backup methods effectively.
Current Control Charge Equalization Algorithm for Adjacent Cell-to-Cell Topology Using Buck-Boost and Dual Half-Bridge Converter

Charge equalization is essential to maximizing the performance and functionality of lithium-ion battery packs. Various equalization topologies have been developed using charge equalization circuits (CEC) integrated within the Battery Man-agement System (BMS). However, when implemented using buck-boost CEC with conventional switching operations, the adjacent cell-to-cell ($AC2C$) topology exhibits the maximum equalization loss. Therefore, a maximum-minimum equalization algorithm is proposed to control the cell current by regulating the control input of the CEC, which will improve the efficiency of the equalization operation. In this work, two types of CECs, namely buck-boost and dual half-bridge, have been investigated for the series AC2C equalization topology, and a comparison study between CECs in terms of per-unit development cost, equalization efficiency, and time for different control approaches has been pre-sented. The detailed mathematical modeling for each CEC type is formulated to replicate the identical equalization operation. The proposed methodology is validated with $a$ 48 V, 20 Ah battery pack developed in the PLECS simulation environment.
Design of a Novel Fourth-Order Closed Loop Voltage Controller of an Optimally Designed Dual Active Bridge DC-DC Step-Up Converter

In this paper, a novel fourth-order voltage controller has been designed for an isolated Dual Active Bridge DC-DC step-up converter for battery discharge applications. State space small signal average modeling of Dual Active Bridge DC-DC converter differs from the conventional DC-DC converters since the leakage inductance of Dual Active Bridge converter is in series with the high-frequency isolation transformer, and hence has a pure alternating ripple current, instead of the large average small ripple current for the traditional DC-DC converters. Generalized Average Modelling using a first harmonic approximation of the state variables for the converter with a high-frequency transformer and a step-up turns ratio ‘N’ is presented. Furthermore, actual frequency response plot of the controller transfer function and its application to actual Dual Active Bridge Converter circuit is simulated in MATLAB Simulink and presented in this paper. The optimal design of the Dual Active Bridge Converter with Single Phase Shift modulation is also discussed in detail for the given specifications. This paper also discusses the suitability of the system for a smooth transition from the constant voltage control of the designed Dual Active Bridge voltage controller to a voltage control of an added Solar Photovoltaic based Boost converter for a Multi-input architecture of Solar Photovoltaic and battery-fed application for the same load with the designed controller.
Detection of Hardware Trojan Horse using Unsupervised Learning Approach

Machine Learning helps in detecting hardware trojan and are successful to a large extent to analyze. The supervised learning technique cannot be applied to huge netlist to categorize the samples. This is with respect to two categories namely infected and Trojan free. Such method is complex. So, the usage of unsupervised approach gives good prediction performance without any labeled data. The unsupervised approach works well with the large number of netlists. This work helps in the detection of trojan using a technique called PCA (Principal Component Analysis) which deals with dimensionality reduction and helps in ease of computation. This is followed by unsupervised clustering algorithm from which the detection of hardware trojans is performed. A comparative analysis of k-means clustering and hierarchical clustering is also carried out. The method is evaluated on Trust-Hub circuits and the results highlight the improvement obtained.
Adaptive Tracking Control of an Omnidirectional Mobile Robot System with Uncertainties and Disturbances

This paper addresses the tracking problem of an omnidirectional mobile robot (OMR), which is a second-order electromechanical system with unmatched uncertainties and disturbances. The control inputs are the currents of the motors, and the control objective is the tracking control of the real-time position and orientation for the robot. The main features of this paper are summarized as follows. (1) Both the kinematic and the dynamic models for a standard OMR are presented, which explicitly defines the unknown physical parameters of the system as well as the specific mathematical form of disturbances. (2) A new OMR control scheme is proposed based on adaptive back-stepping techniques. It is shown that the OMR can asymptotically track the desired trajectory with any orientation. (3) In addition to numerical simulations, a real OMR experiment platform is established. The effectiveness of the proposed control scheme has been verified with both simulation and experimental results.
Using Convolutional Neural Networks for Music Emotion Recognition on Microcontrollers

This paper presents a theoretical overview of music emotion recognition (MER) and its processes. More than 30 studies related to emotion recognition and MER were analyzed. Summaries of information about emotion recognition methods, available audio datasets, and audio parameters used in MER are discussed in this paper. The practical part is focused on a proof of concept for MER using the Convolutional Neural Network model on microcontrollers for realtime music emotion classification in four emotion classes based on a 2D emotion model.
Classifier Fusion Based on Gaussian Mixture Models (GMM) and Support Vector Machine (SVM) in the Function of Classification Emotional Speech, Using Spectral and Prosodic Features

Various types of information are imprinted in the speech signal, which the interlocutor registers with his auditory perceptual system. The number of transmitted messages, hidden in the speech signal, is numerous, because they give a stamp on the complete personality of the speaker: his biological, sociological, psychological and social dimensions. Studying and analyzing the emotional characteristics of the speech signal requires knowledge of several scientific fields, such as information technology, psychology, pedagogy, acoustics, signal processing, linguistics, medicine, artificial intelligence and many others. In the process of processing human speech, in order to determine the emotional state of the speaker, in this paper we used its spectral and prosodic characteristics. As the emotional state of the speaker causes a change in the vocal tract in relation to neutral speech, a change in rhythm, intonation, melodicity, volume and other changes, i.e. adequate changes in the spectral plane, we believe that these characteristics are relevant in the speech signal for determining the emotional state of the speaker. Also, prosodic characteristics related to rhythm, accent, intensity and intonation of speech [3] largely reflect emotional speech. In order to classify the speech signal into a specific emotional group, a fusion of classifiers based on Gaussian Mixture Model was used (GMM) and Support vector machine (SVM). SAVEE emotional speech database was used for training and testing data.
Reconfigurable and Swappable Battery Packs for Electric Vehicles

Electric Vehicle (EV) is getting popular day by day and most of the countries in the world are converting their transport system to electric infrastructure. Battery pack is one of the main sources of energy for EV. Fixed batteries of different power are utilized in different types of EVs. The objective of this research is to explore the potential of reconfigurable battery systems in addressing the limitations of fixed battery sources in electric vehicles (EVs). The investigation involves a comprehensive analysis of the current challenges associated with fixed battery sources, including limited range, long charging times, battery degradation, inflexibility. The research focuses on developing a reconfigurable battery solution that allows for flexible battery configurations, enabling enhanced range, reduced charging times, improved battery performance, interchangeability between different EV models. The methodology encompasses battery design and engineering, integration with EV systems, testing and evaluation of reconfiguration mechanisms, and performance analysis through simulations and experimental validation. The results of this research provide insights into the feasibility, benefits, and contributing to the advancement of sustainable transportation and electric vehicle technology.
Stored Energy Forecasting of Small-Scale Photovoltaic-Pumped Hydro Storage System Based on Prediction of Solar Irradiance, Ambient Temperature, and Rainfall Using LSTM Method

This paper presents the implementation of forecasting of photovoltaic (PV) power and stored energy on small-scale pumped hydro storage (PHS) systems. The proposed forecasting approach considers the results of predicting solar irradiance, ambient temperature, and rainfall. Prediction of these three parameters was done using a one-month weather dataset that was split into 80% for training and 20% data for testing and validation. The prediction algorithm used is the bidirectional long short-term memory (LSTM) method. Furthermore, PV power and stored energy were calculated using power and energy models of the photovoltaic-pumped hydro storage system modified by considering the prediction results of solar irradiance, ambient temperature, and rainfall. The proposed forecasting approach was simulation-tested on a small-scale photovoltaic-pumped hydro storage system with a capacity of PV is 2 KW, as well as a capacity of the upper reservoir is 5 m3, The performance of the forecasting model is done by measuring the mean square error (MSE) and the mean absolute error (MAE) values. From the simulation test, the results obtained that the suggested approach produces PV power forecasting performance with an MSE of 8.942 watts and MAE of 0.044 watts, and excess power forecasting performance with an MSE of 81.203 watts and MAE of 0.075 watts. The stored energy forecasting performance for MSE and MAE parameters are $2.2\times 10^{-7}$ Wh and $7.35\times 10^{-6}$ Wh, respectively.
Non-Conventional Concentric Winding Layout Design of Hairpin Windings for Enhanced Traction Performance of Induction Machines

Future electric vehicle (EV) traction motors require high power density, high efficiency, wider speed range, lower torque ripple, and lower weight and volume. Hairpin (HP) windings are a favored option for traction motor windings; however, their efficiency tends to degrade due to AC losses at high operating speeds. Therefore, HP winding designs that offer higher EM performances, including higher efficiencies in the full operational region, and lower winding weight are necessary for future EVs. In this regard, this paper investigated the arrangement of the HP windings within the stator slot, considering different phases, and utilized an improved winding function-based model and analytical AC loss estimation to propose an optimal concentric winding (CW) configuration for a commercially available 140 kW, 15000 rpm induction machine (IM). According to the results, except for the similar torque capacity in the maximum torque per ampere (MTPA) region, the proposed optimal CW IM configuration demonstrated superior overall performance and characteristics, including output power, torque, and efficiency, across a wide speed range; both MTPA and field weakening regions compared to the IM with distributed windings (DW). Additionally, the proposed CW configuration reduces winding weight by 12.96% compared to the IM with conventional DW, which is a significant advantage in terms of weight and volume reduction.
Exploration of Four Coils Magnetically Coupled Resonant Inductive Power Transfer System with Efficiency Optimization Under Unsymmetrical Power Wiring Structure

The recent investigation of four-coil inductive power transfer (IPT) systems has provided an effective solution for extending the WPT distance. In this study, an equivalent circuit model is established to enhance the transmission efficiency of the four-coil IPT system. The impact of various parameters on power transmission efficiency is analyzed. Moreover, the relationship between the system efficiency, mutual inductances, and operating frequencies is interpreted. The results demonstrate that adjusting the coupling coefficients and increasing the resonant frequencies can optimize the system efficiency. By utilizing the proposed system parameter design, the maximum coil-to-coil efficiency is improved by almost 20%.
Decoupling Control of Single-Stage Multiport Inverter-Fed Motor Drives Using Zero-Sequence Voltage Injection

The single-stage multiport inverter (SSMPI)-fed motor drives enable direct connection from the dc-side energy sources to the ac motor without utilizing any dc/dc converters, which feature high power density and high efficiency of the overall system. However, the output power of each source is highly coupled with the motor control in this topology, which provides poor flexibility of power control. To address this problem, a decoupling control strategy is proposed in this paper. First, an asymmetrical carrier-based modulation strategy is adopted to ensure the high performance of the motor control by fully considering the voltage variation of sources. Then, injecting different values of the zero-sequence voltage adds a degree of freedom for controlling the output power of sources. With the proposed method, the flexible power control is realized without affecting the performance of the motor control. Finally, the effectiveness of the proposed control scheme is verified through experiments on a permanent magnet synchronous motor (PMSM)-based laboratory prototype.
Reconfigurable Mobile Robots Robust Segment Reconfiguration Docking Strategy

A modular reconfigurable robot can change its configuration adaptively according to the different environments and tasks. By adding or subtracting modules or recombining existing modules, the current configuration can be quickly changed to another one adapted to a new environment and tasks. Stable autonomous docking control directly affects the mobility, docking accuracy, and docking success rate of reconfigurable robots, and has become the focus of research in this field. In this paper, a multi-constrained trajectory planning algorithm is designed, and a multi-stage robust reconfiguration strategy is designed on the basis of this optimization algorithm. The strategy is also applied to a chain modular reconfigurable robot independently researched in the laboratory for testing, and the experimental results show that the method in this paper is effective and feasible.
Identification of the Advanced Data Exfiltration by Human Activity Recognition using Transformer

Advanced Persistent Threats (APTs) are extremely dangerous and hidden cyber threats. Even with the unavailability of computing assets and data penetration using cloud computing, APTs still find ways to exploit vulnerabilities. This paper proposes new methods for classifying malicious traffic. Since cyber attacks have not yet been widely studied, they are similar to Human Activity Recognition (HAR). In this sense, we introduce the Transformer, an effective Machine Learning approach, with a new architecture that shows more remarkable effects in the classification of HAR. In preliminary experiments using our model, the classification accuracy reached 91.45%, and the average accuracy reached 91.25 %. We also applied other classification algorithms, such as RNN, CNN, and LSTM, which also presented adequate solutions for the described cyber attack.
Improving Fish Freshness Classification with Fish Eye Segmentation Guidance

Estimating fish freshness plays an essential role in multiple fields, including the food industry. Most existing freshness estimation methods progress in a biochemical manner, which can inevitably cause defects in the fish samples. Although recently advanced image processing-based approaches realize this task from fish images, achieving satisfactory accuracy is still difficult because fish bodies do not often reflect freshness well. In this study, inspired by the fact that fish eyes possess a strong correlation with freshness, we propose a simple yet effective method that learns to classify fish freshness into three predetermined levels from fish eye images. Specifically, we first train a segmentation network to produce a mask that identifies the fish eye area. This mask is then concatenated to the fish eye image as an additional input channel for the classification network to output the freshness level. Consequently, the classification network is explicitly guided to focus more on the fish eye area than on the background. Experimental results on a publicly available fish eye dataset demonstrate that our method contributes to improved freshness classification accuracy in terms of the four types of classification backbones.
Design and Implementation of a Domestic Off-Board Multifunctional Bidirectional Electric Vehicle Charger

This article presents an off-board bidirectional battery charger for electric vehicles (EV s) that has been designed to perform various tasks beyond simply charging electric vehicles (EVs) like Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) technologies and also improving the grid power quality (PQ). During charging mode, the G2V mode of operation takes place. Here, power flows from the utility grid to the vehicle with sinusoidal current and also maintains a unity power factor. Similarly, in discharging mode, the V2G mode of operation takes place. In this case, the power is delivered from the batteries of the EV to the utility grid. During the V2H mode of operation for the period of grid disruptions, EV s provide an uninterrupted power supply to domestic loads. The adopted topology and control technique is implemented to operate the laboratory-developed prototype electric vehicle charger (EVC) to verify the simulation results at various operating conditions.
Empowering Industry 4.0 with Generative and Model-Driven SDK Development

Industry 4.0 developers need high-quality libraries in their preferred programming languages to efficiently and ergonomically create, edit and exchange digital twins as Asset Administration Shell (AAS). Existing specifications of the AAS can not be directly translated into programming code. This leads to many, often manual, re-implementations of the AAS in different projects and programming languages. We present an approach for automatic generation of software libraries based on the formalized AAS meta-model. This shows that the development with our approach is 5× faster for a new language and 10× faster for a new version of the AAS meta-model, respectively.