Miracle-dz/UniFAR-FTUs huggingface dataset:
CNC lathe tool wear analysis using image processing and stray flux

Within the manufacturing industry, the condition of cutting tools directly impacts the quality and costs of machining, which has driven the development of various methodologies to identify and monitor tool wear. In recent years, the stray magnetic flux has started to be used as a physical variable for wear detection, giving good results both individually and in combination with other physical quantities. The present research proposes the analysis of stray magnetic flux signals in a CNC lathe in conjunction with image analysis of the machined surfaces and the cutting tools in order to detect tool flank wear with cutting speed variation in the machining of 6061 aluminum. The results report the correct detection of the different levels of wear, demonstrating the ability of each of the methodologies to detect the level of wear regardless of the cutting speed used for machining, as well as the future improvement with the fusion of the techniques to obtain more reliable results for a robust detection system.
Connection Fault Diagnosis for Series-connected Lithium-ion Battery Based on Curve Similarity Calculation and Curve Conversion

In electric vehicles (EVs), the connection faults in lithium-ion battery packs may cause accelerated battery aging and inconsistency. Timely and effective connection fault diagnosis is of great significance to ensure the safe operation of EVs. This paper proposes a connection fault diagnosis method for series-connected battery packs based on the combination of edit distance on real-sequence (EDR) and charging capacity-voltage (QV) curve conversion. The method can determine the location of the faulty cell and the preliminary quantification of fault degree by calculating the similarity of the cell charging curve. Then, by calculating the conversion parameters, the specific connection resistance value can be calculated and distinguished from the internal resistance. A set of experiments were designed on series-connected batteries to simulate the connection faults with different severity. The experimental results show that the method can effectively locate the connection fault and diagnose its severity in the series-connected batteries without complex battery model.
Control of single-stroke movement of a drum-playing robot by reinforcement learning using a realistic artificial muscle-driven robot

Artificial muscles are advantageous owing to dynamic stiffness; however, the drawback of this is their poor controllability. Here, a reinforcement learning-based control system is proposed and exemplified on a realistic artificial muscle-driven robot. The proposed system suppresses the modeling error, and allows to generate dynamic motion patterns that utilize the body structure and variable stiffness characteristics. As an example application, we consider drum playing. We propose a reinforcement learning-based realistic drum-playing robot. We propose a reinforcement learning-based strategy for this realistic robot and confirm its effectiveness through simulations and experiments.
Digital Twin Approach for Remote Monitoring of Microgrids

Monitoring of remote microgrids helps in providing better service to the consumers. Prior idea of location/nature of fault and/or device malfunction saves a lot of time for the maintenance engineer during outrages. Thus, the off-site monitoring of microgrids ensures the proper operation as well as efficient work execution during clearance of fault as it gives an idea for the visiting engineer about the location of fault and malfunctioning devices. In this paper, a digital twin based scheme is presented for monitoring the power flow of remote microgrid. Digital twin is a real-time, physics based simulation integrated with the real physical system and provides the estimate of different characteristics in real-time. A physics based digital twin of the remote microgrid (wherein DGs are controlled with conventional droop scheme) is modeled using simulation and the power flow of the microgrid is monitored using digital twin model. The scheme is validated on a real-time simulation platform (RTSP) for different type of loads. Different cores of the RTSP are used for implementing the physical microgrid system and digital twin based monitoring scheme.
Efficient Supply chain delivery planning considering dynamic route selection using Ant Colony Optimization

Dynamism is a component that had been part of our world until today's era, a factor that continues to impact or influence on the course of processes prone to variation and adaption, respectively. This study develops a model to address the dynamic scenario that plays into routing selection in a road map scene exposed to changes over time and for which companies and businesses in the sector of delivering goods and services to customers often have to deal with it. The approach aims to empower corporations or industries in the journey of achieving their goals and attain efficiency, sustainability, cost and time reduction, and an augment in profit gain. The model implements Ant colony optimization with evaporation setting as to enhance adaptability over variable and non-static optimal path patterns particularly in a profound location dependent set and distance length cost concerns. The efficient routing selection is analyzed and found to be promising just as described from the outcomes of the model based on the inferred near realistic locations map data. Many researchers have used Ant colony optimization for its capacity to deliver on optimal path finding for various related problems and have proven performance improvement. Further, below are discussed, the problem description, the ant colony optimization concept and algorithm step wise, implementation and results comparison, as well as other illustrative representations and graphs.
On Saturated Linear State Feedback and Anti-Windup Design Using Sector-Based Conditions and Convex Hull Representations

In the field of closed-loop stabilization of linear systems under saturated state feedback, the question of whether a sector-based condition or a convex hull approach provides the largest invariant ellipsoid as an estimate of the system domain of attraction has been drawing great interest. This work presents a proof that, regardless of the system input size, fulfilling a popular sector-based condition for saturated linear feedback design, in fact, implies the feasibility of a well-known polytopic condition obtained through a convex hull representation of the saturated input, resulting in the same contractively invariant ellipsoid as a region of asymptotic stability. The converse is not true, but if the sector-based condition is applied in a sequential manner, then the same invariant ellipsoid determined by the polytopic approach can be obtained in a finite number of steps, for the same saturated linear state feedback. Indeed, this paper presents a complete characterization of those strategies in relation to each other, something that appears to be missing in literature. Those results are derived using a set of stabilizability conditions based on dissipativity theory and can be readily extended to deal with the anti-windup design problem as well.
Comparative Performance Assessment of Predictive Torque Control Strategy for Motor Drive Applications

Recently the majority of research in the field of predictive torque control (PTC) focuses on torque ripple and computational burden reduction. The main disadvantage of the traditional PTC strategy is the high computational time requirement, and it restricts the utilization of the PTC in automotive application. In this paper, a new PTC is used based on the modification of conventional PTC that reduces the computational time. At the same time, the modified PTC proposed in this paper reduces semiconductor losses and thermal stress. Moreover, a comparative analysis between conventional PTC and modified PTC has been conducted in terms of driving, electrical and thermal performance. It is found from a simulation study in MATLAB/Simulink® that the modified PTC reduces the switching loss by up to 20%, while the efficiency is increased by more than 5% compared to a conventional PTC. Finally, a reduction in the heatsink temperature response is also noticed during this assessment.
An interpretation framework for autonomous vehicles decision-making via SHAP and RF

Decision-making for autonomous vehicles is critical to achieving safe and efficient autonomous driving. In recent years, deep reinforcement learning (DRL) techniques have emerged as the most promising way to enable intelligent decision-making. However, DRL with ‘black box’ nature is not widely understood by humans, thus hindering their social acceptance. In this paper, we combine SHapley Additive exPlanation (SHAP) and random forest (RF) techniques to bring transparency to decision-making obtained by DRL. Specifically, we first implement decision-making of autonomous vehicles following in discrete action space based on DRL algorithm with the goal of safety and efficiency. Then we use the SHAP technique to simplify the feature space, which shows that relative distance, longitudinal speed of the ego vehicle, and longitudinal speed of the proceeding vehicle have a critical impact on vehicle following task. Finally, we collect the state-action pairs generated by the DRL model and perform feature filtering, and fit the decision model with an interpretable RF model. The simulation results show that the RF model achieves the behavioral explanation of autonomous vehicle following.
Tracking control of nonholonomic mobile robots with dynamic event-triggered strategy

This paper studies the dynamic event-triggered tracking control of a nonholonomic mobile robot. Firstly, a non-holonomic dynamic model for a mobile robot is suggested. Then, a dynamic event-triggered control strategy with an auxiliary dynamic parameter is proposed for the mobile robot to track the reference trajectory. Moreover, it is proved that the triggering time sequence does not exhibit the Zeno behavior. Finally, the effectiveness of the proposed control mechanism is validated by a numerical example.
Transfer Learning-based Hybrid Modeling Approach for Indoor Temperature Modeling

Indoor temperature modeling has been a vital component to develop accurate digital twins and smart controllers for buildings. Hybrid (also known as gray-box) modeling caught significant attention from the literature for this task. Combining the accumulated physical knowledge we have about thermal behavior with modern data-driven techniques promises more accurate and stable prediction models which can be used in various applications. However, methods such as data-driven parameter optimization and constrained training proposed in the literature show practical limitations such as high computational expense and software incompatibilities. In this paper we propose a transfer learning-based hybrid modeling approach where a CNN-LSTM model is pre-trained with the simulation data and then refined with the real-life data, thus, creating a completely data-driven hybrid model. We compared our approach to the same CNN-LSTM architecture trained only on real-life data. We reported significant accuracy and stability increases with the proposed approach.
Design of customized high-performance on-chip and off-chip Inductor to transmit power in millimetre distance compared with contrasting electromagnetic simulators

This paper designs a customized high-quality inductor for both on-chip and off-chip simulation on an electromagnetic simulator. A customized high-efficient “wireless power transfer inductor” transmits large amounts of wireless energy at low resonant frequencies, including high adaptability. The purpose is to design power transferring, magnetic fields, and transceiver coils. For the on-chip, we are using a silicon substrate because of its low resistance, and the inductor is made of aluminium because, in transmission, it has significant advantages in efficiency and size, even in the short range of millimetres. We use a ferrite core and copper wire as a coil for off-chip. These models can be parameterized for electromagnetic simulation and measurement of test structures. The problems that arise are precisely regulating the inductance and quality factor, which can be solved with various EM simulators such as Sonnet, HFSS and Maxwell, obtaining accurate and stable results. Electromagnetic induction is an essential convention for conducting power from a transmitter to a receiver, which depends on the non-physical interaction of coefficient K. We design resonant coils for a transmitter and receiver that are tuned in resonance to meet the impedance on both sides.
Machine Learning Aided Optimized Modulation in Triple Active Bridge Converter

The triple active bridge (TAB) converter is considered for connecting the different voltage power networks. The circuit circulating current will increase following the voltage difference increase. We propose machine learning (ML) aided modulation to improve the efficiency of the transmission power by suppressing circulating currents in the TAB converter. In the proposed modulation, it is necessary to identify the optimal operating point by varying five control variables. The five variables are the three duty ratios of the inverter voltage and the two phase shifts between ports. The five variables are determined by the port voltages and the input/output powers at each port. However, it is difficult to determine the five control variables mathematically to identify the optimal operating point. Therefore, in the proposed method, an ML model is used to determine the five variables. The model aims to find the optimal operating point based on the condition suppressing circulating currents. Experiments were conducted under the input voltage of 400 V and two output voltages of 250 V while varying the power sharing ratio and load factor ratio. The experimental results show an improvement in the efficiency of about 20.0 % under the light load condition of P2 = P3 = 0.2 kW compared with the general single phase shift modulation, as well as maximum efficiency of about 96.5 % under the full load condition of P2 = P3 = 2.0 kW.
P-VIDO: A Tightly-Coupled Vehicle Dynamics and Plane Priors Extension to Visual-Inertial State Estimator

Traditional monocular visual-inertial odometry (VIO) system cannot be applied to vehicle platforms due to unobservable degenerate motions, which result in poor initialization and low positioning accuracy. We propose the P-VIDO, a fast monocular visual-inertial odometry system with tightly coupled coplanar constraints and vehicle dynamics constraints. Plane landmarks are constructed using 3-point RANSAC methods and introduced into the optimization problem to provide coplanar constraints for point features. This significantly improves the accuracy of the odometry system in a structural environment such as an indoor parking lot. The experiments show that the system is executable in different vehicle motions with higher precision, as far as existing VIO methods are concerned. Besides, the system proposed in the paper can achieve 55fps averagely with a single thread.
Use of Resonant Terms in a 2DOF Control Scheme for the Current Control of an Active Power Filter

This paper proposes a control scheme for the current injected by an Active Power Filter (APF) that has an LCL filter at its output. The proposed structure relies in a Two-Degrees-of-Freedom (2DOF) controller in which resonant terms are included. The 2DOF scheme follows the current reference at the fundamental frequency of the grid and damps the resonance phenomenon of the LCL filter, whereas the resonant regulators allow a proper tracking of the harmonics. This is achieved with only the measurement of the current injected by the APF into the grid, without the need for additional measurements or observers. In order to assess the performance of the controllers, a Hardware-In-the-Loop (HIL) emulation has been carried out. The results show a good time response, allowing the compensation of current disturbances at both the fundamental frequency and its harmonics.
Personal Data Access and Distribution Management Extension to FIWARE

In smart communities which refers to communities that leverage the use of technologies to benefit their citizens, it is necessary to ensure data interoperability among services and secure data operations. Particularly, it is necessary to manage the data path of services based on edge computing to consider the level of privacy between regions and at different network layers. FIWARE, a smart city operating system(OS) that has been recently deployed in several cities, provides an application programming interface (API) based on common rules to ensure interoperability and to enable coordination and distribution of services and data. However, the data distribution control component is assumed to be placed in the cloud, and it does not have a distribution management mechanism. In this study, within the architecture of FIWARE, we have built a mechanism that manages data paths on an edge by using tags of dataflows which is associated with the personal information in the packets. Additionally, we implemented a benchmark and measured a processing delay for gathering personal information. In the simulation environment, the processing time delay was 0.05 seconds by using proposed system when the control component is placed in the egde area.
Coordinated Charging Strategy of Cascaded H-bridge With Bidirectional DC-DC Converter for Supercapacitor Energy Storage Applications

Cascaded H-bridge with bidirectional DC-DC converter (CHBDC) is promising in high voltage supercapacitor (SC) energy storage applications, in terms of multilevel voltage output, modularity, high efficiency, etc. However, the charging process becomes complicated when part of the SC voltages reach the target value ahead of the others. The improper charging may cause the CHBDC over-modulation and even grid current distortion. To solve this problem, this paper analyzes the stable operation area of the CHBDC in this scenario. Based on these analysis, a coordinated charging strategy is also proposed for the CHBDC, by adjusting the reference voltage of each H-bridge unit and the grid power factor. Simulation results show the validity of the proposed charging strategy.
BTI-Aware Cell Characterization based on Neural Network

As semiconductor technology scales down, Bias Temperature instability (BTI) became more important in the presence of reliability of CMOS circuits. Also scaling down of the circuits has more effect on the performance due to the aging effect, leading to an increase in time complexity for cell characterization. In this paper, to cope with the time complexity issues, we propose neural network based cell characterization considering BTI to predict gate delay faster and accurately. Our proposed method extracts the delay time of gates with HSPICE simulation to use as training data for the neural network. The model generated after training with the neural network can predict the gate delay of specific conditions. Experiment results show that our model can reduce cell characterization time 700x faster compared to HSPICE with 98.15% accuracy.
High-Performance Grid Current Feedback Control for Three-Phase Voltage-Source Converter with an LCL Filter Under Distorted Grid Conditions

This paper presents the implementation of the proportional resonant plus multi-resonant (PR-MR) and practical proportional resonant plus multi-resonant (PMR-MR) harmonic controllers in the current control loop to mitigate the high-order harmonic currents. The harmonic controllers, of orders 5th, 7th, 11th, and 13th with proper discretization methods are added to the fundamental controller in the stationary reference frame to mitigate the grid current harmonics caused by the distorted grid voltages. An extensive current control design procedure, proper methods of discretization of the resonant controllers, and system stability analysis are presented. The experimental grid currents show a lower total harmonic distortion close to 1.1% under the distorted grid conditions and comply with the IEEE 1547 standard. The PR-MR and PMR-MR controllers were implemented on the TMDSDOCK28379D 32-bit floating-point digital signal processor. The system is simulated in MAT-LAB/Simulink environment and experimentally implemented on a 7.5 kW three-phase LCL-filtered grid-connected VSC with the proposed control schemes.
Comfort-Aware Trajectory Planning in Autonomous Driving via Multi-Objective Nonlinear Model Predictive Control

Autonomous passenger vehicles are expected to change mobility as we know it. They can potentially offer many advantages over traditional vehicles, but user acceptance is fundamental to benefit from them. In particular, comfort will be one of the most relevant deciding factors. The lack of control over the vehicle's motion will increase the passengers' inability to predict the path, raising their discomfort. The most straightforward way to maximize comfort is to adopt smooth and slow speed and cornering profiles, but the risk is to penalize too much the total travel time. A preliminary study on the optimal trajectory planning for an autonomous vehicle to find the best user-defined trade-off between comfort and travel time is presented. The problem is approached with a Nonlinear Model-Predictive Control strategy. After describing a suitable vehicle model, the complete formulation of the algorithm is presented. A multi-objective analysis is then carried out to obtain the Pareto Front and the Utopian solution for comfort and travel time. Finally, the algorithm is applied to a case study, assessing the overall performance.
Research on Eco-driving Strategy in Urban Scenarios Based on Deep Reinforcement Learning

As vehicle intelligence and connectivity develop by leaps and bounds, high attention has gradually been attracted to the eco-driving. It is significant to investigate eco-driving which is an effective method to improve both fuel economy and mobility in urban scenarios. However, most of the methods of current researches on eco-driving are model-based methods, in which complex traffic scenarios are a great challenge to the construction of models. Therefore, a deep reinforcement learning (DRL) algorithm which is model-free is applied to determine eco-driving strategy. Specifically, this paper proposes a method based on soft actor-critic (SAC) algorithm to realize eco-driving. Firstly, the problem of eco-driving is introduced according to the urban scene. On this basis, SAC algorithm is introduced and SAC model is designed to realize eco-driving. Finally, the method is verified by training and testing. The method based on SAC can effectively realize the eco-driving of vehicles according to the results. Compared with the global optimization algorithm such as simulated annealing (SA), energy consumption is reduced by 11.25%, while the whole decision-making time can be greatly reduced to 16.01%, which is of great significance for practical application.
Homography Theories Used for Image Mapping: A Review

In computer vision, color homography is the key part of geometric methods and is used in camera tuning, stereoscopic viewing, three-dimensional views, and many more. It associates the picture elements of two images. This association can be done only and only if both images are focusing on the same plane but the angle is dissimilar. In this paper, an integrated framework is presented which explains the different phases of the homography estimation system. Benefits and Issues are discussed which can help to analyze this research area. This paper also surveys all the application areas such as mosaicking, image registration, three-dimensional camera pose reconstruction, collision avoidance, and many more. This paper has prepared its content for general questions about homography such as what is homography estimation, what are datasets available, and the best results achieved so far. Finally, the findings of this paper state that solving the color homography issues leads the way to more précised estimation.