Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability Comparison of Grid-Forming Converters with Different Power Calculation Strategies

Grid-forming (GFM) converters, such as virtual synchronous generators (VSGs), behave as voltage sources behind a reactance within a specified frequency range. Therefore, solutions to limit the rapid growth of the converter output current, which may cause over-current tripping during grid faults, are necessary. One effective solution is to use outer dual-loop voltage and current control so that current limitation can be realized by saturating the current reference. In the condition, electrical power (EP) of VSG can be calculated either with the converter output current or with the virtual current from the voltage loop, leading to different small-signal stability for the GFM converter. In this paper, small-signal stability of these two EP calculation methods are analysed and compared. Small-signal eigenvalues analysis shows that using virtual current from outer voltage loop for EP calculation contributes to better small-signal stability as compared to the conventional EP calculation approach, under both the weak grid and the strong grid conditions. Simulation results are provided to validate the effectiveness of the analysis.
A Multi-source Information Fusion Method for Mobile Robot Visual-inertial Navigation

Vision sensors have the advantages of low cost and low structural complexity. However, in certain challenging scenarios, such as high dynamic range and high-speed motion, tracking failures and inaccurate positioning may occur. While a single sensor excels in specific aspects of SLAM technology, the integration of multiple sources of information has become crucial in the field of vision-based SLAM. To address the limitations of pure visual sensors in the SLAM system, a visual-inertial fusion strategy based on multi-sensor fusion is proposed. By incorporating inertial navigation units, the SLAM system gains absolute scale information, and establishing temporal alignment between camera frames enhances calculations. This approach improves the accuracy and robustness of the mobile robot’s positioning and mapping system, enabling the complementary utilization of angular velocity and acceleration data from the IMU and visual camera sensor. Experimental results demonstrate significant enhancements compared to the ORBSLAM2 algorithm, with absolute errors reduced by approximately 25% to 60% and relative errors improved by 70% to 90%. Moreover, the proposed method achieves higher accuracy in calculating attitude changes between adjacent frames.
Closed-Loop Simulation Test Method for Autonomous Vehicles Based on Adversarial Scenarios

Using simulation technology to verify the safety of autonomous vehicles (AVs) is a critical aspect of the development of autonomous driving technology. The primary objective is to reduce the time and cost of AV testing while enhancing testing efficiency. In this study, we propose a closed-loop simulation test method for AVs based on an adversarial scenario generation process. A comprehensive closed-loop simulation system was established, incorporating Simulink, Prescan, Carsim, and hardware-in-the-loop systems.To swiftly identify challenging test scenarios, we formulated an optimization model for safety evaluation, which is based on an improved driving safety field model. We employed the genetic algorithm to effectively solve this optimization problem, allowing for the rapid generation of difficult scenarios.Experimental results demonstrate that our proposed method can effectively generate challenging scenarios, enabling the swift confirmation of the safety performance boundaries of the system under test. This approach significantly improves the overall testing efficiency and provides valuable insights for the advancement of autonomous driving technologies.
Self-Identification of Reluctance Synchronous Machines with Analytical Flux Linkage Prototype Functions

This paper presents a self-identification approach for reluctance synchronous machines (RSMs) in combination with analytical flux linkage prototype functions. Due to the severe magnetic saturation, good machine knowledge is required for high-performance electrical drive systems. The proposed self-identification method injects bipolar voltage pulses to an inverter-fed machine at standstill, without additional test equipment and requirements. Within an extremely short time, nonlinear flux linkages, including the magnetic saturation and cross-coupling effects, can be obtained. Instead of saving the samples as lookup tables (LUTs), they are approximated by analytical flux linkage prototype functions, which are parametrized by few parameters, and continuously differentiable throughout the whole operation range. The effectiveness of the developed self-identification method is experimentally validated for a nonlinear 4.0kW RSM. The results prove (i) the simple and time-saving procedure of the self-identification method, (ii) the high approximation accuracy and (iii) the benefits of the flux linkage prototype functions for real-time applications.
A Computational Drug Repositioning Effort using Patients’ Reviews Dataset

The drug discovery process is one of the core motivations in both medical and, specifically, pharmaceutical disciplines. Due to the nature of the process, it requires an excessive amount of time, clinical experiments, and budget to cover each discovery phase. In this sense, computational drug discovery efforts can shorten the discovery process by providing plausible candidates since many of the attempts fail for several reasons, such as a lack of participants, financial problems, or ineffective results. In this study, the goal is to identify plausible candidate drugs for diseases. To do that, we utilize a personal experience of drugs dataset generated by patients. Beyond the user-generated comments, the users also give a rate between 1 and 10. Since we want to ensure the dataset quality, we first performed sentiment analysis experiments to prove that the reviews/comments are consistent with the given rating score. Then, only the review pairs having an effectiveness rate of 6 or more are selected as pre-filtered drug-disease pairs. We also build a knowledge graph using treatment-related biomedical relations using predications from Semantic Medline Database to identify drug similarities utilizing the Simrank similarity algorithm. As a result, we reported a list of plausible drugs as repurposing/repositioning candidates for further experiments.
Detection and Intervention Engagement Service Development for New Normal Distance Learning

The trend of using video conferencing applications for distance learning has increased during the pandemic and the new normal period. Distance learning offers a solution to overcome distance and time differences through the use of new information technology that can be continuously updated. However, distance learning also presents challenges and problems in engaging learners. It is important to know how the level of learner’s engagement are during distance learning. To determine the engagement level of learners in distance learning, a detector is needed that can reach learners who are not visible to the instructor. Along with detection, interventions to improve engagement in distance learning are also necessary to maintain high levels of learner engagement. Previous studies on the detection of engagement with computer vision based on facial emotion expressions have not implemented interventions in their research. This research accommodates engagement interventions in distance learning based on the level of engagement that has been detected by engagement detection services. Distance learning using this service prototype shows that the level of student engagement is maintained.
Event-Triggered Scheme on Appointed-Time Leader-Following Consensus of Multi-Agent Systems with Prescribed Performance

The appointed-time leader-following consensus (ALFC) problem for second-order multi-agent systems (MASs) with prescribed performance (PP) under directed graphs by means of an event-triggered control (ETC) scheme is investigated in this paper. Based on Lyapunov theory, we prove that the position tracking error reaches an arbitrarily small specified compact set in appointed-time. Besides, the proposed distributed protocol guarantees the PP of the controlled system, which means that the cumulative position difference (CPD) stays within a preset region being determined by a novel performance function. In addition, the proposed event-triggered mechanism sufficiently reduces the frequency of control’s update and no Zeno behavior in the MASs. Theoretical results are supported by simulations.
Dynamic Optimization for Shunt Active Power Filter to Selectively Extract Harmonics with Vector Discrete Fourier Transform

Power quality is essential for the development and utilization of new energy. The extraction of harmonic is the prerequisite of shunt active power filter (SAPF) to suppress harmonics. Selective harmonic extraction methods could extract specific harmonic components individually, which are summed to obtain the harmonic instruction of SAPF. Therefore, these methods are of flexibility and adjustment of each extracted harmonic, and they are more suitable for the case where the power or suppression capacity of SAPF are limited. Among them, recursive discrete Fourier transform (RDFT) is commonly used in SAPF, because of its low computation burden and outstanding selectivity. However, when the loads are switched, it will take one fundamental period to reach its steady state of extraction, which limits the dynamic of SAPF. To alleviate it, a vector DFT (VDFT) is proposed in this paper. By reconfiguring the zeros and poles of RDFT with the synchronous reference frame, the dynamic time of VDFT is shortened to 1/6 fundamental period, while its capacity of single harmonic extraction could be retained. The performance of VDFT is verified with the prototype experiments.
Imitation Learning from Operator Experiences for a Real time CNC Machine Controller

Controlling complex industrial systems can be a challenging task as it requires extensive knowledge and skills that are usually acquired through years of experience. This makes it difficult to program such expertise into machine algorithms. In this paper, we present a use case that demonstrates how we built control algorithms for a CNC machine using historical logging of observations from experts. With the advent of digital technologies, machining parts are now controlled by computer programs that offer high precision and speed. However, unforeseen scenarios can still arise, which demand operators’ attention and intervention, even with finely crafted machine programs. For our experiment, we collected data from a 5-axis Mazak Integrex i500-series CNC machine over a month manufacturing multiple instances of the same part. We collected observational states, which are sensor data that match the information operators receive and output engagement feed rates following the operator’s trajectories. Using behavioral cloning, we built an initial control policy from this data, testing three families of machine learning models: regression models, ensemble methods, and deep neural networks. The results showed that ensemble methods outperformed the baseline model significantly, proving that they have learned useful control patterns. The policies also demonstrated that ML models could eliminate noisy behaviors from operators’ actions. We believe that with interactive demonstrations in the future, these models have the potential to fully mature. Overall, our study demonstrates the feasibility of building control algorithms for complex industrial systems using historical expert demonstrations and machine learning techniques.
A method for planning the trajectory of mobile hydraulic crane booms with a focus on energy efficiency

The paper describes a method for planning energy-efficient trajectories for the booms of hydraulic mobile cranes in (semi)autonomous operation. The method includes a learning step, in which a multi-physics model of the crane is used to calculate weight matrices for the path planning algorithm. This step requires considerable time but adjusts the method to the mechanical structure and hydraulic system of the particular crane. At the implementation step, when the calculated matrices are used to build energy-efficient trajectories, the method is fast enough to be run in real time. The paper considers a control algorithm that allows moving the crane booms automatically according to the built trajectory. The simulation experiments demonstrate energy savings with the median value of approximately 5% for energy-efficient trajectories in comparison with the shortest paths.
LOTS: Litter On The Sand dataset for litter segmentation

The marine ecosystem is threatened by human waste released into the sea. One of the most challenging marine litter to identify and remove are the small particles settled on the sand which may be ingested by local fauna or cause damage to the marine ecosystem. Those particles are not easy to identify because they get confused with maritime/natural material, natural elements such as shells, stones or others, which can not be classified as "litter". In this work we present a dataset of Litter On The Sand (LOTS), with images of clean, dirty and wavy sand from 3 different beaches.
CG-based dataset generation and adversarial image conversion for deep cucumber recognition

This paper deals with deep cucumber recognition using CG (Computer Graphics)-based dataset generation. The variety and the size of the dataset are crucial in deep learning. Although there are many public datasets for common situations like traffic scenes, we need to make a dataset for a particular scene like cucumber farms. As it is costly and time-consuming to annotate much data manually, we proposed generating images by CG and converting them to realistic ones using adversarial learning approaches. We compare several image conversion methods using real cucumber plant images.
Adjusting the Number of In-use Cells for Higher Efficiency of A DC Solid State Transformer

The efficiency of a dc solid state transformer (DCSST) is important to the future medium voltage dc (MVDC) systems. With the development of high-voltage semiconductor devices, a MV-DCSST can be built by fewer cascaded power conversion cells. In this case, the number of in-use (in operation) cells will have a significant effect on the DCSST efficiency. The relationship between the number of in-use cells and the efficiency of the DCSST is derived. Adjusting the number of in-use cells according to the load condition is proposed to enhance the DCSST efficiency. Experiment is provided to verify the proposed concept.
Parametric Data-Driven Reduced Order Model using neural networks and manifold-based interpolation*

The simulation of many industrial processes can be prohibitively expensive, due to the complexity of the mathematical models used to describe them. This makes the control of such processes a challenging task, especially when the models are parametric. In light of this, we present a data-driven framework to build a parametric reduced-order model (ROM) through the combination of singular value decomposition (SVD) and neural networks. A manifold-based nonlinear interpolation is used to deal with the effect of the parameter change on the spatial modes. On the other hand, we train a neural network to resolve the dynamics of the system. The framework presented in this work is non-intrusive, i.e., it does not require access to the governing equations of the original model. The performance of the framework is evaluated on a process of convective drying of wood chip particles. Results show that the ROM developed can accurately predict the dynamics of the process for a new set of parameters.
Plug-in Electric Vehicles Grid Impact on Short-Term Voltage Stability in Renewable Rich Grids

This paper explores the effect of plug-in electric vehicles (PEVs) on the short-term voltage stability (STVS) of modern power grids. The PEVs are basically seen from the grid as portable batteries on wheels. There is a potential opportunity to get the benefit of them to support grid security, e.g., short-term voltage stability. This paper assesses the impact of various PEV modes on the STVS of the system. The assessment is done using the detailed model of the transmission and distribution system with high penetration of renewable generation, i.e., large-scale PV. The PEV charging demand is difficult to predict due to uncertain drivers’ daily distance, home arrival time, and departure time. Therefore, a realistic PEV charging demand profile is used. Furthermore, PEVs could behave differently during charging, e.g., static passive load, current source battery, and voltage source battery. From the analysis, it is evident that the different possible behaviors affect the STVS margin. This paper highlights possible services that PEVs might provide when they act as voltage sources. It also emphasizes the importance of the appropriate PEV model for stability analysis.
High Frequency Induction Heating of Non-magnetic Metals with 24 VDC for a Terrestrial Antenna

Heat generation accounts for a significant amount of global energy consumption, induction heating can serve as an alternative to conventional gas heating in some applications. Advances in wide band-gap semiconductors like Gallium Nitride enabled high switching speeds, for power semiconductors, in the range of 1 MHz, opening up new possibilities, such as the induction heating of non magnetic metals. Typical induction heating systems operate in the range of 50 kHz due to high switching losses with conventional silicon semiconductors. Induction heating is a highly efficient method for heating metal objects as it directly generates heat within the material. A induction heating system for a terrestrial antenna of the EIVE-T research project, which operates at low ambient temperatures of -25 °C was developed with an aim of keeping the temperature-sensitive semiconductor components at a steady 25 °C, by the means of induction heating of the aluminum mounting plate. The induction heating system developed in this work, operates with an input voltage of 24 VDS and reaches an input power of 237 W. The system uses a half-bridge GaN power converter to achieve high switching frequencies of 0.8 MHz to heat various non magnetic and magnetic metals. Induction heating efficiencies can reach up to 49.1% for magnetic metals and up to 43% for non-magnetic metals. The study examines the effectiveness of generating heat in various types of metals, both non-magnetic and magnetic, by analyzing induction heating efficiencies and the effect of the distance between the coil and the metal being heated on different types of metals.
QaQ: Robust 6D Pose Estimation via Quality-Assessed RGB-D Fusion

RGB-D 6D pose estimation has recently drawn great research attention thanks to the complementary depth information. Whereas, the depth and the color image are often noisy in real industrial scenarios. Therefore, it becomes challenging for many existing methods that fuse equally RGB and depth features. In this paper, we present a novel fusion design to adaptively merge RGB-D cues. Specifically, we created a Quality-assessment block that estimates the global quality of the input modalities. This quality represented as an α parameter is then used to reinforce the fusion. We have thus found a simple and effective way to improve the robustness to low-quality inputs in terms of Depth and RGB. Extensive experiments on 6D pose estimation demonstrate the efficiency of our method, especially when noise is present in the input.
Baseline Load Estimation for Demand Response Based on Blockchain and Neural Networks

With the large-scale integration of distributed new energy to the power grid, the power system increasingly relies on demand response to solve the needs of power grid regulation. In demand response, how to estimate the baseline load of response resources is of great significance. With the deepening of baseline load research, the traditional baseline load estimation methods are becoming more and more difficult to apply. To this end, this paper proposes a solution to the shortcomings of traditional methods using blockchain combined with private data. Firstly, traditional baseline load estimation is not effective due to the lack of a large amount of private data. The neural network combined with private data can be used to obtain a high-precision baseline load. Secondly, to address the trust issues caused by the use of private data, the blockchain is used for encrypted storage and regular spot checks are conducted on the owners to achieve mutual trust between the two parties involved. Finally, through the experimental simulation of chemical plants and electric vehicles, the effectiveness of the proposed scheme is verified.
Sensorless Control for IPMSM in Entire Speed Range Based on HF Voltage Signal Injection and Virtual BEMF

In this paper, a sensorless control algorithm for an interior permanent-magnet synchronous motor (IPMSM) using high-frequency (HF) voltage signal injection with square wave reference and virtual back electromotive force (BEMF) is proposed. This paper modifies the voltage model of IPMSM using virtual BEMF. Based on the modified voltage model, the proposed algorithm estimates the rotor position using virtual BEMF of fundamental frequency and HF. Using the proposed method, sensorless control in full speed range is possible with no conventional transition methods between zero/low speed and medium/high speed. The proposed algorithm is verified by simulation and experimental results with 1.7 kW IPMSM. The proposed method shows identical stable operation performance with the conventional transition method with a much simpler implementation.
Map-matching on Wireless Traffic Sensor Data with a Sequence-to-Sequence Model

Map-matching plays an essential role in many location-based applications. It seeks to translate a sequence of timestamped location measurements, which may originate from GPS, Bluetooth, or cellular sources, into the actual routes that moving objects follow on the underlying digital road network. Some existing work focus on map-matching methods based on Hidden Markov Models. While powerful, these methods are computationally demanding and require highly accurate location information. In contrast, neural network-based methods offer the ability to handle more complex data sources, but face challenges when applied to large-scale road networks. In this research, we delve into the task of map-matching using wireless traffic sensor data, specifically Bluetooth, in the context of expansive road networks. We introduce a Turn-Based Map-Matching (TBMM) model, built upon a Sequence-to-Sequence framework. This model accepts a sequence of Bluetooth readings as input and generates a sequence of successive turns with a predicted start road segment. As the sequence of turns is generated, the corresponding route is concurrently reconstructed, adhering to the topological structure of the underlying road network. Furthermore, we employ a two-step training approach to optimize our model. We begin by pre-training the model by minimizing cross-entropy loss. Subsequently, we deploy reinforcement learning to fine-tune the model, thereby further enhancing its performance. Our experimental study shows the promising performance of our TBMM model compared with two state-of-the-art solutions, achieving approximately 98% in precision, recall, and F1-score, demonstrating the potential of our approach in the domain of map-matching.