Miracle-dz/UniFAR-FTUs huggingface dataset:
A Combinational Power Circuit-based Submodule Topology for MMC With Shoot-through and Selfbalancing Capability

The upper and lower power switches of sub modules (SMs) in Modular Multilevel Converter (MMC) could be switched on simultaneously by disturbances which could destroy the SM circuit and cause damage to MMC. To improve reliability, this paper proposes a novel SM topology based on the combinational power circuit. It consists of three circuits: the half-bridge circuit, clamping circuit and energy-transfer circuit. It overcomes the shoot-through problem using the impedance-source network. The fluctuation of SM capacitor voltages can be controlled by the hysteresis control strategy in each SM. The SMs are self-balanced by making the fluctuation of each SM capacitor voltage consistent. The circulating harmonic current can be small even without any circulating current suppression strategy (CCSS). Without the need for voltage balancing control and CCSS, it greatly reduces the computation load. Finally, a five-level single-phase experimental platform is established to verify the feasibility of MMC based on the proposed SM topology.
Double Vector Model Predictive Current Control for the Semi-controlled Open-winding PMSG Generation System

To reduce the computational burden of the conventional model predictive current control (MPCC), improve the steady state performance of single vector MPCC for the semi-controlled open winding PMSG (OW-PMSG), and further suppress zero sequence current simultaneously, a double vector MPCC method for semi-controlled OW-PMSG generation system is proposed in this session. In this method, the first voltage vector of controllable convertor is selected according to the sector location of the reference voltage vector. For the second voltage vector, the selection method-based deadbeat voltage vector sector is designed to reduce the range of the candidate voltage vector. Finally, the simulation results validate effectiveness of proposed method.
Novel Delay Elements for Bundled-Data Transfer Circuits Based on Two-Phase Handshaking Protocols

A number of delay elements have been well studied since they are a basic component in VLSI designs. However, it is not clear how to design the optimum delay elements for the two-phase handshaking protocol in which both the rising delay and the falling delay must be larger than the critical path delay of the corresponding combinational circuit. In this paper, novel delay elements for bundled-data transfer circuits based on the two-phase handshaking protocol are proposed. Generally, critical paths of a combinational circuit may be varied due to environmental changes. The optimum static delay elements should be selected considering all the environmental changes. In addition, delay variations strongly depend on the transistor size and the circuit structure. In this paper, a circuit structure which contains several deep stack transistors with the minimum channel length is proposed in order to reduce the differences of the variation features between the delay elements and the corresponding combinational circuits. Several evaluation results are shown using the 28nm process technology.
A plug-and-play home energy management algorithm using optimization and machine learning techniques

A smart home is considered as an automated residential house that is provided with distributed energy resources and a home energy management system (HEMS). The distributed energy resources comprise PV solar panels and battery storage unit, in the smart homes in this study. In the literature, HEMSs apply optimization algorithms to efficiently plan and control the PV-storage, for the day ahead, to minimize daily electricity cost. This is a sequential stochastic decision making problem, which is computationally intensive. Thus, it is required to develop a computationally efficient approach. Here, we apply a recurrent neural network (RNN) to deal with the sequential decision-making problem. The RNN is trained offline, on the historical data of end-users’ demand, PV generation, time of use tariff and optimal state of charge of the battery storage. Here, optimal state of charge trace is generated by solving a mixed integer linear program, generated from the historical demand and PV traces and tariffs, with the aim of minimizing daily electricity cost. The trained RNN is called policy function approximation (PFA), and its output is filtered by a control policy, to derive efficient and feasible day-ahead state of charge. Furthermore, knowing that there are always new end-users installing PV-storage systems, that don’t have historical data of their own, we propose a computationally efficient and close-to-optimal plug-and-play planning and control algorithm for their HEMSs. Performance of the proposed algorithm is then evaluated in comparison with the optimal strategies, through numerical studies.
A Reactive and Model-Based Approach for Developing Internet-of-Things Systems

Software has a longstanding association with a state of crisis considering its success rate. The explosion of Internet-connected devices – Internet-of-Things – adds to the complexity of software systems. The particular characteristics of these systems, such as its large-scale and heterogeneity, pose increasingly new challenges. Model-based approaches have been widely used as a mechanism to abstract low-level programming details and processes. By using such approaches, and leveraging concepts as reactive design, visual notations, and live programming, we believe to be able to reduce the complexity of creating, operate/monitor and evolve such systems. The main objective of this Ph.D. is to delve into the software engineering practices for developing IoT systems and systems of systems, exploiting models as a suitable abstraction, expecting to reduce the complexity of managing most of the software development lifecycle that targets IoT systems and to develop the prototype that will aid on the validation of such approach.
A Study of Data Fusion for Predicting Novel Activity in Enterprise Cyber-Security

Modern computer networks allow for the collection of vast amounts of data. A wide variety of sources record data relating to different aspects of computer and network activity. This wealth of available data, coupled with the persistent rise in successful cyber-security breaches, motivates the need for data-driven approaches to complement existing cyber-defence systems. Although obtainable, most of this data remains unexploited due to issues of data collection and privacy concerns. The majority of research has therefore been constrained to utilise limited data sets, usually obtained from only one of the many available data sources. We use a recently assembled public domain data set, which associates data from multiple sources in a real-world enterprise computer network, to demonstrate the advantages of data and entity fusion for cyber-security. We formulate an anomaly detection task employing time-delayed labels, which enables the use of supervised learning as a means of predicting novel activity. Our results show that an appropriate fusion of data from multiple sources and entities improves predictive accuracy.
High Resolution Direct Writing Liquid Metal Patterns for Printed Stretchable Electronics

A rapid and inexpensive method to produce stretchable liquid metal electronics with high resolution is introduced. Here, liquid phase gallium-indium (GaIn) alloy is firstly patterned on a pre-stretched elastomer, followed by a natural shrinking process to achieve high resolution stretchable electronic components and interconnects. The idea is inspired from the inflation and deflation of patterned balloons. By a cascaded direct writing and transfer printing step, the printing resolution increases by 20 times on pre-stretched substrates. This rapid prototyping approach paves a new way for microscale printed stretchable electronics.
An All-Silicon Double Differential MEMS Accelerometer with Improved Thermal Stability

Although MEMS technologies are developing rapidly, MEMS accelerometers still can not compete with macro electro-mechanical accelerometers in the performance of bias temperature stability and long-term bias repeatability. Unlike former reported literatures trying to use innovative package technologies or self-calibration methods to solve this issue, this work was attempting to employ double differential configuration to improve accelerometers' thermal stability. Slanted-beam sandwich structures were chosen to construct a novel double differential scheme, and this scheme is believed to be more suitable for high-precision and high-stable applications. The working principle and Finite Element Modeling (FEM) of our proposed accelerometer were presented. Then, the proposed fabrication processes and major fabrication challenges were also addressed. Preliminary measurement results of the discrete version experimentally demonstrate the significant improvement of the accelerometer's thermal stability.
Multivariable controller for stationary flat plate solar collectors*

The water flow rate control, which is the principal control loop required to operate stationary solar fields, can be as simple as designing a PID controller for a unique pump whose behavior can be approximated by a first-order system with delay. Nevertheless, when the water flow rate is manipulated with multiple actuators (pumps or valves), the problem presents some challenges due to hydraulic interactions. This paper presents a multivariable controller scheme to deal with the control of a solar field with five pumping systems. Simulation and experimental results are included to demonstrate the effectiveness of the proposed controller.
Performance Analysis of Full Duplex D2D in Opportunistic Spectrum Access

Opportunistic Spectrum Access (OSA) allows an efficient use of spectrum based on share-it or use-it principle and can be a viable solution for the challenging problem of spectrum scarcity. Emerging systems have been proposed for OSA, where primary users (PU) have guaranteed interference protection from secondary users (SU). The potential of Full Duplex (FD) and Device-to-device (D2D) technologies in 5G has proven to be promising for increasing data rates and network capacity. In this article using stochastic geometry and random graphs, we model and assess the D2D operations in full Duplex/half Duplex mode for SUs, while protecting the PU’s transmission by defining the exclusion zone (EZ). Depending on the location and transmit power of D2D users, the induced aggregate interference should not violate the interference threshold for EZ of PUs. For this, we characterize the interference from D2D links and derive the probability for successful D2D users for half-duplex and full duplex modes. Analyses is further supported by extensive simulations results.
A Novel Nonlinear Control Technique With Its Application to Magnetic Levitated Systems

In this paper, we propose a novel controller design technique which is applicable to nonlinear systems. Based on the classical power shaping methodology, we establish some sufficient conditions which are able to guarantee the stability of the resulting closed-loop system. By relaxing some preliminary conditions, the particular expression of the nonlinear controller is given and the asymptotic stability of desired equilibria can be proved according to the Lyapunov theory. To motivate the application of the new proposed controller design technique and illustrate its feasibility, we verify the proposed methodology with a magnetic levitated system. Moreover, simulation results are presented to show the performance of the designed controller.
Conversion of the Vietnammese Grammar into Sign Language Structure using the Example-Based Machine Translation Algorithm

This study has been trying to find the existence of correlation Vietnamese and Vietnamese sign language (VSL) based on kinds of words. The conversion of the Vietnamese sentence structure into the shortened structure of the VSL is normally without rules. In order to solve that problem, a method of machine translation has been applied. The correlation between these two languages will be analyzed by word processing tools and synthesized from the VSL experts’ corrections to develop training datasets. The study has compared the use of words or types of words to construct the training datasets. It has been also evaluated using the example-based machine translation algorithm (EBMT) and the Translation Error Rate (TER). The results indicate that the accuracy of conversion is 97.42% which can use for converting of the Vietnamese text into VSL.
Design and Implementation of Contactless Power Array Track with Segment-excited Inductively Coupled Structure

This paper is aimed to utilize the technology of contactless power transmission and implement contactless electric vehicle (EV) power track transfer system with segment-excited inductively coupled structure. A five-level inverter is used as the primary exciting source. In order to increase lateral displacement and longitudinal misalignment tolerances between the EV and the track, the toroidal inductively coupled structure with a uniform magnetic field is proposed. The segment-excited control has been added to the overall power track which consists of pad arrays to reduce power loss and increase the transmission efficiency of the system. According to theoretical analysis, the appropriate resonant circuits are utilized for improving power transmission ability and efficiency. According to the experimental results under 10 cm air-gap, the output power in the system is reached 854 W with a transmission efficiency of 80%, and the maximum transmission efficiency is reached 85% at 431 W output power.
Design of Fluorine-Ion-Based Junction Termination Extension for Vertical GaN Schottky Rectifier

High-quality bulk GaN substrate has led to the demonstration of vertical GaN power devices. Aiming at realizing effective edge termination and avoiding premature breakdown, we propose a new fluorine-ion-based junction termination extension (F-JTE) method, which is well suited for vertical GaN devices in which the selective p-type region is difficult to form. The influences of the F-JTE and its key parameters (e.g. dose, thickness and width) on the electric field distribution and breakdown voltage have been extensively investigated and revealed by TCAD simulations. The negatively charged F-JTE region can effectively spread the electric field away from the main junction, leading to increased breakdown voltage. With a partial recess step, a double-zone JTE with a graded distribution of charges can be formed, which can further improve the breakdown voltage and process latitude.
Inference of Gene Regulations Between Multiple Activators/Inhibitors and Singular Genes

A gene regulatory relation often has multiple activators or inhibitors. But most of current methods only focus on inferring gene regulatory relationships between two genes. We developed a new qualitative method for representing dynamic gene regulatory relations, and algorithms for identifying gene regulations with multiple regulators. All the algorithms are implemented in a program called GeneNetFinder (http://wilab.inha.ac.kr/genenetfinder) and tested on several gene expression data. The approach and the program would be useful for identifying gene regulations with multiple activator/inhibitors.
Word Embedding Representation with Synthetic Position and Context Information for Relation Extraction

In recent years, various knowledge bases have been built and widely used in different natural language possessing tasks. And relation extraction is an effective way to enrich knowledge bases. But in most existing relation extraction methods, they obtain word embedding from pre-trained Word2vec or GloVe, which don't consider the difference of word in different sentences. But, such a fact cannot be ignored, that is, the same word in different contexts or in different position in a sentence has different meanings. So, we propose an approach to get word embedding representation with synthetic context and position information and call it semantic word embedding. After getting semantic word embedding, we can get sentence-level representation by simple average-pooling rather than complex architecture of convolutional neural network. Furthermore, we apply the semantic word embedding representation to the relation extraction task of Natural Language Processing. The experimental results show that the performance of the proposed method on the popular benchmark dataset is better than the state-of-the-art CNN-based approach.
Performing a Virtual Field Test of a New Monitoring Method for Smart Power Grids

This paper presents a virtual field test to evaluate the performance of a new grid monitoring method using artificial neural networks (ANN) under realistic operating conditions. The ANN monitoring method is able to accurately estimate voltage magnitudes of distribution grids with high penetration of distributed generators without the need for a redundant amount of measurements. The simulation framework OpSim allows a logical separation of a grid simulator and a simple distribution grid control center to create a realistic testing environment. A CIGRE benchmark grid with diverse distributed energy resources and corresponding time series is used in a grid simulator. Measurements are derived from the simulator and sent to the control center via the simulation message bus. The ANN monitoring method makes use of the measurements to estimate all bus voltage magnitudes. These can, in turn, be used by a transformer tap controller to control the overall voltage profile of the grid to stay within desired limits. Transformer tap set points are returned to the grid simulator if limits are violated. The performance of both the monitoring method and its impact on the tap controller are evaluated under normal operation, bad data and delayed measurement cases. Results show that the ANN monitoring method works reliably and accurately.
Stability-Oriented Design for LCL-LC-Trap Filters in Grid-Connected Applications Considering Certain Variation of Grid Impedance

In the distributed power generation systems, to suppress the switching harmonics, LCL filter combined with LC trap is attracting wide attention. However, in the digital control system, due to the folding effect of extra resonance poles, such LCL -LC- Trap filter behaves quite different from LCL filter. If the design and control of LCL -LC- Trap filter do not seriously consider the folding effect, the inverter can even be unstable. It is quite necessary to formulate a robust design and control dedicated for LCL -LC- Trap filter. First, this study analyzes the resonance of LCL -LC- Trap filter and shows the difference from LCL filter. To suppress the extra peak and phase sag caused by the folding effect, a passive resistor is then added. Unlike the typical passive damping, power loss of the passive resistor for suppressing the folding effect is minimal. By solving the folding effect, LCL -LC-Trap filter is treated as LCL filter within the Nyquist frequency. Then, a stability-oriented design based on resonance-frequency selection is provided, with which the inverter with LCL -LC- Trap filter can achieve the optimal phase margin in the default case while being robust for filter parameters variation. At last, results have been provided to verify the analysis and design.
Nonlinear Dimensionality Reduction with Judicial Document Learning

This paper investigates the applications of NLP and machine learning techniques to judicial decision making.These legal documents are often represented by n-grams, term frequency-inverse document frequency (TF-IDF) or other methods, which lead to high feature representation of documents.Often, the number of labeled judicial documents are less than the dimensionality of features of judicial documents. It will degrade the prediction performance by directly using these extracted features from text. This paper studies the applications of various linear and non-linear dimensionality reduction techniques for judicial decision making. The extensive empirical experiments have been carried out to evaluate the manifold learning based dimensionality reduction method for judicial documents classification.
A Current-feed Single-switch Resonant Converter with Constant Frequency Operation

This paper proposes a current-feed single-switch resonant converter with a low-cost and simple scheme. By alternating between forward mode and resonant mode, the transformer is reset. The energy can be delivered to the output without adding an auxiliary circuit and the turn-off switching loss is therefore reduced for the primary switch. Also, the proposed circuit structure contains only one single switch. Thus, the output voltage for a resonant converter can be regulated with constant frequency operation by taking use of a simpler structure, and the transformer magnetizing energy is utilized to achieve higher power efficiency. Moreover, the resonant inductor is placed in the secondary circuit to effectively alleviate the oscillation problem. In addition to detailed theoretical analysis, an experimental prototype is built to verify the feasibility and effectiveness of the proposed concept.
Voltage Source Active Damper Applied to Resonance Suppression of Multi-inverter Grid-connected

Multi-inverter grid-connected systems are usually in the weak grid environment where the grid impedance changes greatly, which results in a strong coupling between the inverter and the grid impedance in the grid-connected power generation system, causing the resonance problem. A lot of researches have been conducted on current source active damper, but it is difficult to achieve a small virtual harmonic impedance. So its ability to suppress resonance in grid-connected systems is limited. Regarding the issues above, this article presents a control strategy for a voltage source active damper. The voltage source active damper can virtualize the smaller harmonic impedance to reshape the impedance of the point of common coupling, which effectively suppresses the harmonic and resonance of the system from the system level Finally, taking dual-inverter system as an example, the resonance suppression effect is theoretically analyzed, and the feasibility and effectiveness of the proposed voltage source active damper control method are verified through experiments.
An Automatic Method for Semantic Focal Feature Point Tracking of 3D Human Model in Motion Sequence

In this paper, a method, for automatically identifying and tracking garment related semantic focal feature points on 3D human model in motion sequence, is proposed. We consider the problem of automatic focal feature point identification and tracking when non-rigid shape deformation is occurred. The main contribution is that a novel method of tracking focal feature points when the human avatar move in front of depth camera. Firstly, we learn a regression analysis model that derives the relationship between sampled and focal feature points. Secondly, we build a model of correspondence maps to calculate the tracking results. The method can track garment-related feature points for different people in different motion and shape. We demonstrate on a wide variety of experiments that our approach leads to a significant identification and tracking result with input depth sequences.