Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Model Predictive Control for Three-level inverter

Model predictive control (MPC) has been gaining significant attention due to its ease to consider multiple nonlinear constraints and outstanding dynamic response. However, finite control set mode predictive control (FCS-MPC) suffers from huge demand of computing resources and cumbersome tuning of weighting factors, which are obstacle for its application. To resolve this contradiction, this paper presents an effective method to carefully select the voltage vectors aiming to simplify the traditional model predictive current control (MPCC) and make weighting factor tuning got simplified. Firstly, a voltage prediction model is used to replace the traditional current prediction model by equivalent changes. Then, a new cost function and fast optimization method based on the geometric characteristics of the space vector of the output voltage of the three-level NPC inverter is proposed. Finally, the proposed control strategy is verified on the hardware-in-the-loop simulation platform that it can greatly improve the computing efficiency of MPCC while maintaining the current tracking performance with good dynamic and static performance.
Trust your BMS: Designing a Lightweight Authentication Architecture for Industrial Networks

With the advent of clean energy awareness and systems that rely on extensive battery use, Battery Management Systems (BMSs) have seen an increased inclusion in modern complex systems like electric vehicles and power grids. This has presented a new set of security-related challenges. Security concerns arise when BMSs are intended to extend their communication with external systems, as their interaction can leave many backdoors open that potential attackers could exploit. Consequently, we explore and propose a security architecture solution intended for the authentication and session key establishment between BMS and other system devices. The aim of the proposed architecture is to be applicable in different industrial settings and systems, while at the same time keeping the design lightweight in nature. To achieve this, we use the implicit certificates with the ECQV schema. We show the applicability of the design through a security and performance analysis of our implemented test setup.
Dynamic Online VNF Placement with Different Protection Schemes in a MEC Environment

The Multi-access Edge Computing (MEC) architecture is made up of geographically distributed edge servers so that computing capabilities are provisioned at the network edge, close to the end users. Network Function Virtualization (NFV), when combined with MEC, provides network services in the form of Service Function Chains (SFC) with low latency. In the design of NFV-based 5G networks, the trade-off between the cost of resource deployment and the effective provisioning of services must be considered. In this work, we analyze the impact of having different MEC locations when considering the provision of SFCs in a dynamic scenario (and thus also address VNF placement). In order to deal with infrastructure failures, it is of great importance to employ robust and resilient network strategies. To safeguard SFCs against failures, various protection techniques can be applied. We use two protection methods, namely, dedicated VNF protection and shared VNF protection, under the assumption of single network failures. The operational performances of different approaches are evaluated in terms of blocking ratio and end-to-end delay, both for the whole network and for different services, and we analyze whether it is better to distribute computing servers among a few MEC sites or among a higher number.
Outlier-Detection-Based EKF for Power Systems

This paper investigates the state estimation problem for the power systems suffering from outliers, and a mixed outlier detection (MOD) method in extended Kalman filter (EKF) is proposed. Firstly, the vulnerability of traditional EKF method suffering from outliers is analyzed based on a generator model, as well as the motivation of using outlier detection method. Then, an outlier-detection-based EKF (ODB-EKF) is obtained by applying the proposed MOD to the recursive process of EKF. In this case, a time-varying detection matrix is established based on the parts of predictions and innovations where projection statistics and least squares methods are used to detect the outliers in two parts respectively. Subsequently, the data will be updated according to the results of the MOD method. Finally, the numerical simulation results based on the parameters of the IEEE 39 bus test system show the good performance of the proposed ODB-EKF against the outliers.
Modular Battery Energy Storage Systems for Available Energy Increase

The aim of this work is to dive into the available energy of different configurations of battery packs, a vital factor when it comes to improving the driving range of electric vehicles. To that end, two different storage system topologies are considered: non-modular and modular batteries. Each of them with passive or active balancing strategies. To achieve realistic results, a reduced-order electrochemical model is used. Cell-to-cell capacity variations are introduced to represent non-uniformities inside the battery packs. Although many different capacity distributions might be considered, the scope of this paper has been limited to classical normal distribution. The results of this analysis show that the available energy can be increased using modular battery solutions.
Linear-nonlinear Learning Feedforward Control for PMSM Based on Deterministic Learning

In this paper, a linear-nonlinear learning feedforward controller is proposed for the recurrent trajectory tracking control of permanent magnet synchronous motor (PMSM) system. Based on the simplified model of PMSM, a linear adaptive feedforward controller is proposed to address the main tracking error which is proportional to the speed of the motor. To address the tracking error caused by the nonlinear friction, a deterministic learning-based radial basis function neural network (RBFNN) feedforward controller is proposed and combined with the linear feedforward controller. As a result of the coordination between the linear feedforward controller and RBFNN controller, the neuron number and computational load are reduced effectively. Under the persistent excitation (PE) condition, the parameters of the proposed composite feedforward controller will converge to fixed values which represent the learned knowledge, i.e., the real dynamics of the PMSM system along the reference trajectory. Therefore, by recalling the learned knowledge, the adaptive process is no longer required when the motor repeats the same trajectory tracking task. Experimental results demonstrate the effectiveness of the proposed feedforward controller.
Defective Surface Detection based on Improved Faster R-CNN

In modern manufacturing, quality inspection of object surfaces has already become indispensable in the production. Faster Region Convolutional Neural Network (Faster R-CNN), a deep learning object detection algorithm, has been gradually applied in the field of inspection, but it is of low accuracy in steel surface defect detection. In this paper, a detection method based on improved Faster R-CNN is proposed. In the method, a modified backbone network extracts features from images, deformable convolution kernels replace conventional convolution kernels to make location more precise, and the multi-scale feature layer extracts feature maps of defects in different scales. In the experiment, the solution comes to a mean Average Precision (mAP) of 0.774 on NEU-DET dataset, exceeding the original Faster RCNN model of 0.7 substantially.
Reconstruction of Two-Dimensional Dielectric Objects with the Conditional Generative Adversarial Network

In recent years, deep learning technology is becoming an increasingly important tool to solve inverse scattering problems (ISPs). Due to the existence of multiple scattering, ISPs are highly nonlinear. Traditional methods to solve such problems require a lot of computing costs and are often time-consuming. The traditional Convolution neural network (CNN) is not very accurate. In addition, it can only be used to solve specific targets. In order to improve its accuracy and generalization ability, this paper proposes a deep learning method to improve the solution of ISPs, which combines the conditional general adversarial network (CGAN) with CNN. CGAN is an improvement based on GAN. By adding additional condition information to the generator and discriminator of the original GAN, the unsupervised learning is transformed into supervised learning, and the condition generation model is realized. This paper gives the steps of this method, which divides the model reconstruction into two steps. In the first step, CNN is used to retrieve the initial contrast (dielectric constant) of the scatterer from the measured scattering data to obtain the rough image of the scatterer. In the second step, CGAN is used to enhance the resolution of the rough image. The reconstruction results were better than those without CGAN.
Quadrotor digital twin platform based on Unity

With the continuous deepening of practical requirements in engineering operations, the specific requirements of application scenarios have continuously improved the control requirements of the quadrotor. Therefore, it is necessary to establish virtual entity models for different flight missions, and build an accurate digital twin system platform by analyzing the four rotors entity model and interacting with the virtual entity. Through the data fusion of the twin virtual and physical entity, the quadrotor is driven to perform the task. This paper introduces the digital twin model into the quadrotor, analyzes the concept and model of the digital twin, and how to realize the twin virtual body that includes the geometry, data, tasks, and physics of the quad-rotor, and builds the Unity remote virtual platform. The validity of the task is verified by the corresponding data and control, which proves the feasibility of the design platform.
Direct Interfacing of Average-Value Models of VSCs in PSCAD/EMTDC

Voltage-source converters (VSCs) are widely utilized in power systems. Due to their high-frequency switching, discrete detailed models of VSCs are computationally expensive in system-level simulations, and their average-value models (AVMs) have proven indispensable for fast/efficient studies. Conventional AVMs of VSCs use dependent current/voltage sources to interface with external circuits. In PSCAD/EMTDC, with a non-iterative solution, the interfacing variables are computed based on the values of the input voltages/currents from the previous time-step. This one-time-step delay can make the results numerically inaccurate/unstable when large time-steps are used in simulations. In this paper, an AVM is developed for VSCs that is directly interfaced with external circuits without delays to allow large time-step. This is done by formulating the equivalent conductance matrix of the VSC AVM which is merged into (and solved simultaneously with) the rest of the network nodal equations. The new directly-interfaced AVM of VSCs is verified in PSCAD/EMTDC against the classic dependent-source-based AVM and is demonstrated to outperform the existing approach in terms of numerical accuracy at large time-steps.
Enhancing CAD-integrated automatic feature recognition of weld joints with GPU-accelerated multi-directional slicing

In this paper, a GPU implementation for a previously proposed weld feature recognition in CAD is presented. The intention was to support downstream manufacturing process planning for large welded structures and automate feature recognition processes, essentially saving what could accumulate to thousands of man-hours spent. However, when running on larger and more complex structures, computation can still reach hours. The GPU implementation presented in this paper aims to further accelerate this process to make it more user-friendly while simultaneously providing an outline for the underlying implementation processes into CAD environments. To do this, Siemens NX is used as the CAD framework with the NXOpen API allowing for functions and tools to be accessed in a programmatic manner. The GPU utilization can then be imported through the Python Numba library along with its CUDA programming architecture. The proposed implementation is shown to perform more than a thousand times faster than its CPU serial execution counterpart for the most complex models. While for the most simple model, the CPU algorithm slightly outperforms the GPU implementation due to the just-in-time compilation nature of Numba.
Finite-Time Sideslip Differentiator-Based LOS Guidance for Robust Path Following of Snake Robots

This paper presents a finite-time sideslip differentiator-based line-of-sight (LOS) guidance method for robust path following of snake robots. Firstly, finite-time stable sideslip differentiator and adaptive LOS guidance method are proposed to counteract sideslip drift caused by cross-track velocity. The proposed differentiator can accurately observe the cross-track error and sideslip angle for snake robots to avoid errors caused by calculating sideslip angle approximately. In our method, the designed piecewise auxiliary function guarantees the finite-time stability of position errors. Secondly, for the case of external disturbances and state constraints, a Barrier Lyapunov function-based backstepping adaptive path following controller is presented to improve the robot's robustness. The uniform ultimate boundedness of the closed-loop system is proved by analyzing stability. Additionally, a gait frequency adjustment-based virtual velocity control input is derived to achieve the exponential convergence of the tangential velocity. At last, the availability and superiority of this work are shown through simulation and experiment results.
Beam Scanning One-Dimensional Leaky-Wave Antenna (Invited)

Although the beam of a leaky-wave antenna (LWA) scans with frequency, steering through the broadside is usually challenging. Various methods have been developed to overcome this limitation. An effective method is using a composite right/left-handed abbreviated as CRLH, structure to design an LWA. In this paper, a CRLH LWA with two different patches is discussed. A rectangular slot is etched on one patch, and the other patch is shorted to the ground with a via at each edge. The radiated beam scans from −46° to +67° when the frequency is swept from 30 to 53 GHz.
Design of the Humanoid Robot TOCABI

The rapidly improving mechanical systems and control algorithms of humanoid robots brings them ever-closer to integration with humans in-situ. As this trend begins a process of research robots to general products, the dissemination of design decisions and application-specific compromises becomes as equally important as the underlying technology itself. Like-wise, the newly discovered applications and societal integration raise new questions for research, including how a humanoid robot should look - and what technological innovations are needed to support such look. While the uncanny valley is often the forefront of humanoid robot design discussions, we present a new full-size humanoid robot named TOCABI, that side-steps the valley and brings forth the question of academic challenges needed to support a new paradigm of industrial design for humanoid robots.
Day-Ahead Scheduling of Integrated Energy Systems Considering Carbon Emissions

The forecast-based day-ahead scheduling can effectively reduce the integrated energy systems (IES) operating cost. In this paper, a day-ahead IES scheduling problem considering carbon emissions is studied. Firstly, an IES system model is established, and the optimization objectives of operating cost and carbon emissions, energy balance equations and constraints are established according to the model. A day-ahead scheduling operating cost considering the existence of prediction errors is established. Then, based on the proposed model, the dayahead scheduling problem of IES considering carbon emissions is formulated as a multi-objective optimization. The problem can be readily solved by the non-dominated sorting genetic algorithm-II (NSGA-II). Finally, some simulations are carried out, and the results show that the effectiveness of the proposed day-ahead scheduling strategy, reducing the operating cost by 45.82%.
Design Oriented Steady State Analysis of Series and Parallel Resonant Converters

High efficiency, high power density and high performance DC-DC converters are the need of the hour in electric vehicle, consumer electronics and telecommunications industry. Resonant converters have proved to be very efficient even for high power applications due to their low switching losses. However, their design and control is still a challenging process. This paper presents the steady state analysis and design of series and parallel resonant converters based on fundamental harmonic approximation (FHA) technique and highlights the importance of load dependent properties in the design of resonant converters.
A Dual-Band Bandstop Filter Based on Ring Resonator

By added two extra two-section open-ended stubs at two terminal loads, a dual-band bandstop filter with ring type resonator structure is introduced in this letter. There are 3 transmission zeros and 11 transmission poles in a single frequency period, therefore, the proposed topology could not only provide controllable ripple level of passband, but also maintain high rejection level and frequency selectivity in the stopband. All the design parameters are derived through mathematical calculation under the condition of return loss RL= 20 dB and stopband rejection SR= 45 dB at the center frequency f0 = 2 GHz. Finally, an experimental circuit is fabricated for verification and the measured results meet the simulation results very well.
N-level GaN Transistor Model for Fast Simulation of Electric Vehicle based Power Electronics Systems

Accurate and fast power loss estimation models based on a good understanding of the occuring loss mechanisms are crucial to achieve high efficiency, high power density and to take full advantage of the superior characteristics of Gallium Nitride (GaN) devices compared to silicon (Si) devices. In this article, a N-level GaN model for loss calculation of a half-bridge circuit with inductive or resistive-inductive load focussed on high simulation speed is presented. Therefore, a novel method, combining an efficient simulation structure with a finely fractioned and accurate power loss calculation, is used. A detailed analysis of GaN loss mechanisms including static-, dynamic-$R_{\mathrm{dson}}$ and deadtime conduction loss as well as switching loss for different types of switching transitions is conducted. Following, the model, in which circuit simulation and loss calculation are uncoupled in order to reduce the computational costs while remaining the accuracy with respect to the manufacturer Spice model, is presented. The loss calculation part of the model includes mathematical, physical and look-up table based sub-models. The model is finally validated by comparing it to the manufacturer Spice model. By using the proposed model, computational costs are reduced by more than 92% at an error of less than 2% in comparison to the same simulation performed with the manufacturer Spice model.
The Effect of Transformer Interwinding Capacitance on Hard-Switched Converter Operation

This paper presents the effect of the transformer interwinding capacitance on the voltage waveform of the primary side switches in a hard-switched full-bridge forward converter. The OFF-state voltage of the interwinding capacitance and the primary switches is shown to be a function of two factors: the transformer turns-ratio and the ratio of the primary switch output capacitance to the transformer interwinding capacitance. It is proved through calculation and measurement that the OFF-state voltage across the primary switches shares the input voltage evenly, by splitting the output inductor in two and placing them across both output terminals before the output capacitor. The solution also severely reduces the voltage variation across the interwinding capacitance, which reduces the generated noise in the converter. The difference between the calculated and measured voltage-levels of the primary switches is up to 3.8%, effectively proving the validity of the calculation methods.
Determination of young's modulus of cortical bone directly from computed tomography: A rabbit model

Abstract The determination of inhomogeneous material properties of bone in finite element analysis is currently based on two converting formulae that convert the computed tomography number into bone density and then into Young's modulus. However, there are variations between studies and no standardized set of formulae exists. The objective of the current study was to propose a procedure for the determination of Young's modulus of cortical bone directly from computed tomography using a rabbit model. Three‐point bending experiments and finite element analyses were used to derive a formula for converting the computed tomography number into Young's modulus. The formula was found to be E=0.8324×(CT)1.057, where E is the Young's modulus, and CT is the computed tomography number. The correlation coefficient between finite element analysis results based on the above formula and the experimental results was found to be 0.973. The proposed procedure was found to be feasible, and it has the advantage of saving computational time. Similar conversion formulae can be derived for human bone when cadaveric bones are available. Such formulae can simplify the procedure for determining the inhomogeneous material properties of cortical bone directly from computed tomography.
Robust Analysis for Mechanism and Behavior Co-optimization of High-performance Legged Robots

This paper presents a novel application of robust analysis on the mechanism and behavior co-optimization of a virtual high-performance monopedal robot. The analysis is tailored for the design of high-performance legged robots, and is based on realistic models with a large number of parameters, that are designed to achieve a large variety of tasks. Robust analysis takes into consideration the effects that real imperfections can have on the performance of the real design; something which is neglected in most design studies and can be crucial for certain applications. The results demonstrate that typical optimization approaches tend to over-optimize the model, which results in a theoretical performance that is practically impossible to achieve in reality. Instead, the proposed methodology takes into consideration imperfections of the real world to obtain an average performance that is less sensitive to these uncertainties, and can lead to robot designs that are closer to their design expectations.