Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Miniaturized Ultra-High Voltage and High Power Supply

A miniaturized ultra-high voltage and high power supply (uHV power supply) is proposed in this paper by choosing the full-wave Cockcroft-Walton voltage multiplier. The voltage multiplier on the secondary side of main power transformer and the isolated resonant current-fed inverter on the primary side are described in detail, including the condition of high voltage rectifier diode working in soft recovery, the design of isolated high power resonant current-fed inverter and its corresponding control mode under the former condition. Additionally, the simulation results of the proposed uHV power supply show that the voltage multiplier diode always works in the soft recovery switching state, and the output voltage ripple peak-to-peak value is smaller than 20V, that is, there is significant improvement in the operating frequency and decrease in the output voltage ripple, in agreement with the expected miniaturization purpose of uHV power supply.
Educational Robotics: Platforms, Competitions and Expected Learning Outcomes

Motivated by the recent explosion of interest around Educational Robotics (ER), this paper attempts to re-approach this area by suggesting new ways of thinking and exploring the related concepts. The contribution of the paper is fourfold. First, future readers can use this paper as a reference point for exploring the expected learning outcomes of educational robotics. From an exhaustive list of potential learning gains, we propose a set of six learning outcomes that can offer a starting point for a viable model for the design of robotic activities. Second, the paper aims to serve as a survey for the most recent ER platforms. Driven by the growing number of available robotics platforms, we have gathered the most recent ER kits. We also propose a new way to categorize the platforms, free from their manufacturers’ vague age boundaries. The proposed categories, including No Code, Basic Code, and Advanced Code, are derived from the prior knowledge and the programming skills that a student needs to use them efficiently. Third, as the number of ER competitions, and tournaments increases in parallel with ER platforms’ increase, the paper presents and analyses the most popular robotic events. Robotics competitions encourage participants to develop and showcase their skills while promoting specific learning outcomes. The paper aims to provide an overview of those structures and discuss their efficacy. Finally, the paper explores the educational aspects of the presented ER competitions and their correlation with the six proposed learning outcomes. This raises the question of which primary features compose a competition and achieve its’ pedagogical goals. This paper is the first study that correlates potential learning gains with ER competitions to the best of our knowledge.
3-D FEM Analysis of Loss and Temperature Distribution in Steady State for 550kV Oil Immersed Current Transformer

High voltage (HV) current transformer with oil-impregnated paper insulation is one of the most important electrical apparatus in power substation, where the temperature distribution as well as maximum hot-spot temperature have significant impacts on its long-term safe operation. In the current work, the loss and temperature distribution of 550kV inverted current transformer with oil-impregnated paper insulation are investigated by three-dimensional (3-D) finite element method (FEM) and computation. The electro-magnetic-thermal field coupling model is designed according to the rated operation condition. The computational results show that the Joule loss of conductive rod on the primary side is 180W which is the main heat source of the transformer in rated operation. The dielectric loss of insulating layer and the eddy current loss of cores are 63.2W and 69.3W, respectively, which cannot be ignored. The hot-spot temperature of current transformer is near the core. And the maximum temperature rise is obtained as 31K relevant to the rated current of 4000A.
POSTER: Feature Selection to Optimize DoS Detection in Wireless Sensor Networks

Denial of Service is the most common attack in Wireless Sensor Networks (WSNs). Decision Trees (DT) and Artificial Neural Network (ANN) are used to detect attackers' signature. The most relevant features and the most dangerous attacks are selected from the WSN dataset. Feature selection reduces the time needed to learn the attackers' signature and improves the speed of detection as it relies on a reduced set of features. Results show that both DT and ANN achieved the best results when only the relevant features are used and the most dangerous attacks are considered. DT achieved 99.83% classification accuracy, 0.998 True Positive (TP) rate and 0.014 False Positive (FP) rate. The best DT has 463 nodes and 232 leaves. The best ANN model achieves 99.76% accuracy, 0.998 TP, 0.004 FP, and it has 28 neurons which is considerably smaller than the best DT.
Research on Mechanism Design and Gesture Recognition Application of High Biomimetic Anthropomorphic Robotic Hand

Since traditional dexterous hand has high cost, low degree of freedom, complicated control, our research benefits from human hand physiological structure, designs and prototypes a highly biomimetic anthropomorphic robotic hand, mimics the salient features of the human hand. We believe that the biomechanics of human hand is an essential component of the hand dexterity and can be replicated with highly biomimetic design. To this end, we first analyze human hand from engineering point of view, replicate the important biomechanical features with conventional mechanical design. Then, we reinterpret the essential biomechanical advantages of the human hand from roboticist's perspective and design a biomimetic robotic hand that closely mimics its human counterpart with artificial joint capsules, crocheted ligaments and tendons, laser-cut extensor hood, and elastic pulley mechanisms. To this end, we identify the workspaces of the fingertip by trajectory experiment, apply the robotic hand in gesture recognition experiment, which collecting the EMG signal through the MYO Armband and controlling the robotic hand to observe the performance of the biomimetic robotic hand. The experiment demonstrate that our design can be tele-operated to grasp and manipulate daily objects.
Incentives for Demand Response and Flexibility Services Procured from Energy Storage Systems

With increasing penetration of renewable energy resources and uncertainty in electricity market prices, Energy Storage Systems (ESS) can play an important role by providing the Distribution System Operator (DSO) with Demand Response (DR) and flexibility services. This work proposes a bilevel optimization approach to determine the incentives for DR and flexibility offered by the DSO (leader) considering ESS as service providers (followers). The proposed model preserves the participants’ rights to control their flexible resources while ensuring monetary benefits for them.
Opinion Leader Identification and Reliability Tracking Based on Optimal Synthesis in the Social Network

In view of the simplified superposition problem during the opinion leader identification process and it is difficult to form a system between identification and evaluation, this paper researches the opinion leader identification and reliability tracking method based on optimal synthesis. We propose an improved index construction model, to measure overall node characteristics in the social network during two periods of event. Then we propose an optimal synthesis method to identify the opinion leader, and based on it, we construct opinion leader Reliability Index as an evaluation basis to realize the opinion leader reliability tracking. This method could effectively identify and evaluate opinion leaders. The experiment result shows that 77% of the opinion leaders satisfy the reliability tracking evaluation criteria, which effectively validates the beneficial effect of optimal synthesis and the feasibility of the identification-tracking system.
Experimental Evaluation of Software Aging Effects in a Container-Based Virtualization Platform

Cloud-based architectures have grown in recent years, especially the interest in container-based solutions have sharply increased by enterprises worldwide. Containers are a form of lightweight virtualization that can be used to provide cloud services. Adopting this kind of technology in a bare- metal context is becoming strong because they can offer many benefits, like performance efficiency and costs reduction. Docker is a widespread platform for the creation and management of containers. As in any computational cloud service, Docker environments must deal with the intensive workload and may have a long-term life cycle, which might trigger some problems that compromise the system dependability. The software aging phenomenon is one of these likely problems. It is a process of cumulative errors or system misbehavior that leads to application failures and performance degradation throughout its runtime. This paper aims to monitor and evaluate software aging effects on the Docker platform in a cloud computing environment. We conducted two experimental studies with automated workloads to simulate containers’ life cycle and the intensive use of Docker features, while the system was monitored. The results show high resource consumption by the operating system’s network utility, in addition to memory fragmentation in the sub-processes of the Docker platform. Trends of increasing resident memory consumption were also observed in one of these scenarios.
Sidelobe Coherency Recovery Technique of Interpulse Diversity based on Inverse Filtering

Transmitting the pulse-to-pulse agile waveforms will break up the sidelobe coherency of impulse responses, which makes the static clutter fail to be completely suppressed, and has a negative impact on radar detection. In this paper, inspired by inverse filtering, we propose an effective method for designing pulse-compression filters that recover the sidelobe coherency of the agile waveforms. By selecting an appropriate window function in the frequency-domain, the impulse responses can be all identical while achieving marked sidelobe level improvement. Furthermore, we analyse the echoes of targets with different speeds and point out the situation which would happen in modern radar application. Simulations are conducted to verify the effectiveness of this method.
An Intuitive and Accurate EMI Model for a Modular Multilevel Converter

Modular multilevel converter (MMC) has numerous sub-modules (SMs) that are complex electromagnetic interference (EMI) sources. It poses a challenge on the EMI analysis of MMC. Motivated by the challenge, this article presents an intuitive and accurate EMI model of MMC based on the superposition theorem. The stray parameters of MMC system are extracted to characterize the high-frequency characteristics of the circuit. By modeling the SMs as individual EMI sources, an EMI equivalent circuit is derived which can intuitively tell the conducted EMI principles of MMC. The EMI model based on the EMI equivalent circuit can accurately predict the EMI results of MMC without a great deal of calculation. Simulation results verify the effectiveness and validity of the proposed EMI model.
An Investigation on Novel Dielectric Phenomenon of Thermochromic RTV Composite: Role of Phase Transition

Dielectric properties of thermochromic RTV (room temperature vulcanized silicon rubber) composite were investigated and a novel dielectric phenomenon was explored. It was found that in the temperature range of thermochromism, tanδ and AC breakdown strength both decreased conspicuously with temperature, whilst those of pristine RTV changed slightly. Phase transition from solid to liquid with temperature during thermochromism was confirmed by DSC (differential scanning calorimeter) and was proposed to be the possible origin of novel dielectric phenomenon. The temperature dependence of tanδ is explained by a combination of decreased resistance to the reorientation of polar molecules due to collapse of intermolecular force and increased cavity volume due to solid-to-liquid phase transition. Meanwhile, the electric strength of thermochromic fillers decreases with the solid-to-liquid phase transition, facilitating the early breakdown of thermochromic fillers and, consequently, leading to the sharp decrease of AC breakdown strength of thermochromic RTV composite. In comparison, no phase transition was found in pristine RTV with increased temperature, which leads to different temperature dependence of tanδ and AC breakdown strength from thermochromic RTV composite.
Robust Point Set Registration Based on Semantic Information

Point cloud registration a challenging task in situations with poor initial value and scenarios with limited geometric structure. In these cases, the correct correspondence between two point clouds is unknown and difficult to establish. To cope with this problem, the semantic of partial points is introduced in this paper. Firstly, the semantic information is used to find more reasonable correspondence, i.e. semantic point pairs. Secondly, we formulate a novel objective function to integrate the matching error of semantic point pairs as guidance of registration. Thirdly, a hyperparameter is applied to balance the confidence of semantic point pairs. At last, a novel algorithm under the ICP framework is presented to optimize the rigid transformation iteratively. The evaluation of KITTI data set reveals the robustness and accuracy of our method in the complex scenes mentioned above.
Fault Current Negative Contribution Method for Inverter-Based Distributed Generators Under Grid Unbalanced Fault

With the rapid increase of embedded inverter-based distributed generators (IBDGs), the active distributed network (ADN) is facing security and protection challenges because IBDGs will contribute extra fault current. In contrary to the existing method that IBDGs provide varying degrees of positive contribution to system fault current, in this article, a fault current negative contribution (FCNC) method is proposed for IBDGs to reduce the system fault current lower than the situation where without IBDGs embedded. In the proposed method, a maximum current phase angle adjustment (MCPAA) strategy is conducted for IBDGs to maximize its negative contribution, thus to minimize the system fault current. In order to obtain a more precise current phase angle, a voltage phase angle determination (VPAD) strategy is also formulated by considering the voltage angle difference on the electrical distance between IBDGs and fault bus. Extensive tests and relevant results have validated the feasibility of FCNC method to achieve a negative contribution of IBDGs on system fault current.
Research on the Control System Based on Internal Model Decoupling for Toroidal Motor

Based on the analysis of the mathematical model of toroidal motor, it is found that the inductance of the toroidal motor is a periodic variable parameter and there is a phenomenon of current cross coupling. With the increase of the speed, the coupling is more serious, which affects the output performance of the toroidal motor system. Therefore, the internal model control is introduced into the current loop. The internal model decoupling controller is designed according to the mathematical model, and then the simulation model is built. Compared with the traditional PI control, it is found that this method has good current decoupling effect and can effectively improve the dynamic performance of the toroidal motor.
Observation of Spatial and Temporal Evolution of Stem near the Electrode in a 1-m Air Gap

Stem is a special short bright zone for steamer-to-leader transition in long air gap discharges, whose characteristics play an important role in leader inception and development. The corona streamer currents rooted in the stem region increase the thermal energy, resulting in an increase in the gas temperature due to Joule heating. In this work, we report the stem near the electrode in a 1-m air gap under positive impulse observed using a Mach-Zehnder interferometer with a time resolution of several microseconds, which provides a better diagnostic method for stems. The experimental observation shows that usually more than one stem appears near the electrode, which is important for corona streamer currents assignment to stem heating. One stem elongates at a speed of about 104 m/s, while other stems are suppressed. Usually one stem forks to two or more stems, and extends in a straight or curved form.
Face Recognition and Rehabilitation: A Wearable Assistive and Training System for Prosopagnosia

The design and implementation of an integrated wearable face recognition and training system for prosopagnosia patients are presented. The purpose of this assistive technology is to provide real-time memory assistance and long-term rehabilitation. The real-time face recognition mode provides audio and visual notification of people who interact with the subject, while the at-home training mode combines features of mnemonic and perceptual training to help with prosopagnosia rehabilitation. In addition, a custom eye tracker is developed to determine the person whom the subject is making eye contact with within a crowd. Using the inverted face effect to mimic the difficulties of prosopagnosia patients, clinically healthy participants have shown improvements in their face-naming abilities. Early results indicate the system’s potential to enrich the well-being of prosopagnosia patients.
Modeling robot co-representation: state-of-the-art, open issues, and predictive learning as a possible framework

Robots are getting increasingly more present in many spheres of human life, making the need for robots that can successfully engage in natural social interactions with humans paramount. Successful human-robot interaction could be achieved more effectively if robots could act predictably and could predict the humans' actions. If robots could represent human partners and generate behaviors that are in line with the partners' expectations based on human's mental models of interdependent action, human agents would be able to apply predictive and adaptive mechanisms acquired in human interactions to interact with robots effectively. How could robots be predictable and be capable of predicting human behavior? We propose that this could be achieved by having an internal representation of both oneself and the other agent, that is by equipping the robot with the ability to co-represent. Here, co-representation refers to the representation of the partner's actions alongside one's own actions. Although co-representation constitutes an essential process for successful human social interaction, as it supports understanding of others' actions, to date co-representation processes have only scarcely been integrated into robotic platforms. We highlight the state-of-the-art findings on co-representation in social robotics, discuss current research limitations and open issues for creating computational models of co-representation in robots, and put forward the idea that predictive learning might constitute a particularly promising framework to build models of co-representing robots. Overall, in this article, we offer an integrated view of the state-of-the-art findings in robotics literature on co-representation and outline directions for future research, with the aim to boost success in building robots equipped with co-representation models fit for smooth social interactions.
Multiple Transmission Resonance Modes in Heterostructure Photonic Crystal

In this paper, we exploit multiple transmission resonance modes in heterostructure photonic crystal using the transfer matrix method. The building blocks of the proposed structures are composed of periodic photonic crystals with period number $\pmb{n}$. By assembling the building blocks in proper order, we observe multiple transmission resonance modes in the spectrum of the heterostructure photonic crystal under the normal incidence condition. The amount of the multiple transmission resonance modes is adjustable by merely altering the period number $\pmb{N}$ of the building blocks. Furthermore, we investigate the influence of the dielectric materials index on the transmission peaks. The proposed heterostructure structure is sensitive to the change of material index, which has a potential application as an index sensor in the field of bio-photonics. In short, this work is a promising candidate as an on-chip multichannel filter and a sensor for future optical communication design.
A Computational Approach for Modeling Partial Discharge Inception in Air Insulation Systems

Simulation software for estimating the basic inception characteristics of air and surface discharges, namely inception voltage and electric field, is presented. The simulation software, called CSPDI, utilizes an electrostatic solver using the Boundary Element Method and integrates physical modeling of partial discharge inception, based on the growth of an equivalent electron avalanche, together with advanced numerical methods. Simulation results are validated against experimental results obtained for short gaps in air alone and bridged by an insulating surface. A very good agreement is shown to exist between computed and measured values of inception voltage, especially with decreasing gap length and for negative discharges.
Anomaly Detection with Machine Learning in the Presence of Extreme Value - A Review Paper

Currently, anomaly detection is receiving a great deal of attention due to its importance in many real-world applications especially in data analytics. Anomaly detection has two important purposes in data pre-processing stages: one is to detect and attempts to eliminate them if and only if it is really outlier, while the other requires attention be paid to valid outlier (extreme value) because extreme value themselves carry the significant and critical information, such as extreme weather events. It will lead to significant harm if not detected and treated properly. This paper begins with the summary of anomaly detection, challenges, type of anomalies, extreme value followed by machine learning algorithms approach that deal with anomaly detection. Finally, we provide a conclusion that discusses about the important of detecting clearly between outlier or extreme value in data pre-processing stage, so that an effective and efficient prediction can be achieved later in modelling stage.
Electric Vehicle P2P Electricity Transaction Model Based on Superconducting Energy Storage and Consortium Blockchain

Compared with the traditional centralized electricity trading model, P2P electricity trading can effectively reduce the loss of electricity caused by the transmission process and improve the user's energy consumption experience. However, due to security constraints such as power flow overruns and grid blockages, P2P power transactions may not be able to proceed normally. To solve this, a superconducting energy storage unit is introduced to store surplus electric energy and intelligently adjust the electric energy distribution according to the actual situation. Considering that the centralized scheduling optimization method cannot be applied to multi-agent real-world scenarios, the blockchain is selected as the underlying data architecture. Simulation and analysis results show that the introduction of energy storage technology can improve the regional economy, and blockchain technology reduces the difficulty of implementing P2P transactions.