Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Minimum Power Loss in Series-Series Compensated Wireless Power Transfer System under ZVS Conditions

Tight voltage/current regulation and high efficiency are fundamental objectives of wireless power transfer systems (WPTSs) as power supplies. Although the well-established impedance matching control attempts to achieve high efficiency, the associated extra dc-dc converters or hard switching in WPTSs reduce the overall system efficiency, particularly for high-power applications. Commonly, achieving ZVS of all switches is an effective way to improve the transfer efficiency in a WPTS. In this paper, under ZVS conditions of all switches, the distribution of the system power loss is investigated and the optimal operating point with the minimum power loss is derived through detailed power loss analysis. A 500-W experimental prototype has been built to verify the correctness of derivation. With a coupling coefficient k of 0.2 or 0.15, the maximum transfer efficiency of the whole system can reach 96.8% or 95.0% under the power rating of 288W (rated load) as well as 92.4% or 88.7% in the case of light load (9% of 288W).
Discovering Research Areas from Patents: A Case Study in Autonomous Vehicles Industry

Seven research areas introduced by the ‘Autonomous Systems’ research lab provide research areas required to enable the autonomous vehicle industry. For ensuring the validity of the research areas with the baseline, we apply Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) on the US patents containing ‘Autonomous Vehicles’ to identify keywords and research areas of relevant technologies. Keyword clustering and TF-IDF are repeatedly applied to the retrieved keywords to further filter out irrelevant words. Coherence values for LSA and LDA are evaluated to determine an adequate number of topics that need to be generated. We found that results from LSA provide a list of technologies already included in the baseline while topics from LDA provide associated keywords to support defining each technology. We conclude the numbers and topics provided by the baseline model closely represent the industry of autonomous vehicles but the identified topics from us provide a significant extension in research areas. The resulting research areas may provide overviews and guidelines on the autonomous vehicles industry for researchers and institutes
Reinforcement Learning Assisted Impersonation Attack Detection in Device-to-Device Communications

In device-to-device (D2D) communications, the channel gain between a transmitter and a receiver is difficult to predict due to channel variations. Hence, an attacker can easily perform an impersonation attack between two authentic D2D users. As a countermeasure, we propose a reinforcement learning-based technique that guarantees identification of the impersonator based on channel gains. To show the merit of our technique, we report its performance in terms of false alarm rate, miss-detection rate, and average error rate. The secret key generation rate is also determined under the impersonation attack based on physical layer security.
Application of TIS Method in Modeling Switched Inductor Buck-Boost Converter

Switched inductor (SI) cell can provide steeper voltage gain to traditional converters. The switched inductor buck boost converter (SIBB) is the research object of this paper. A fast modeling method to SIBB converter is proposed according to the equivalence between the SIBB and the Tapped Inductor Buck-Boost Converter (TIBB). Born of the equivalence of SIBB converter and TIBB converter, the recently emerging Tapped Inductor Switcher Switched Flow Graph (TIS-SFG) modeling method can be of great help in its fast modeling process. The derived model of SIBB converter was confirmed by simulations and laboratorial build prototype tests.
Application of Autonomous Group Particle Swarm Optimization for Optimal PMUs Placement

The optimal location of phase measurement units (PMUs) indicates the strategic placement of PM Us to obtain full observability of the power systems with a reduced number of PMUs. It is necessary to have a strategic placement due to the economic or technical limitations that hinder the placement of PMU s in each bus. A modified version of particle swarm optimization called Autonomous Particles Groups for Particle Swarm optimization (AGPSO) is proposed in this paper, for placing the PMUs that ensure full power system observability in the presence of zero injection bus and single PMU loss, while maximizing the measurement redundancy. The proposed AGPSO has been applied to the IEEE 14, 24 and NE 39-bus. The outcomes show that the number of PMU s is similar to or less than the corresponding results of recently published papers, while the system topological observability is complete.
Volatile-Nonvolatile Memory Network for Progressive Image Super-Resolution

Single-image super-resolution, i.e., reconstructing a high-resolution image from a low-resolution image, is a critical concern in many computer vision applications. Recent deep learning-based image super-resolution methods employ massive numbers of model parameters to obtain quality gain. However, this leads to increased model size and high computational complexity. To mitigate this, some methods employ recursive parameter-sharing for better parameter efficiency. Nevertheless, their designs do not adequately exploit the potential of the recursive operation. In this paper, we propose a novel super-resolution method, called a volatile-nonvolatile memory network (VMNet), to maximize the usefulness of the recursive architecture. Specifically, we design two central components called volatile and nonvolatile memories. By means of these, the recursive feature extraction portion of our model performs effective recursive operations that gradually enhance image quality. Through extensive experiments on <inline-formula> <tex-math notation="LaTeX">$\times 2$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\times 3$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula> super-resolution tasks, we demonstrate that our method outperforms existing state-of-the-art methods in terms of image quality and complexity via stable progressive super-resolution.
An Improved SPICE Model of SiC MOSFET by Output Nonlinear Capacitor Optimization

Simulating the characteristics of SiC MOSFET is of crucial importance to its application. Due to the fast switching speed of SiC MOSFET, accurate modeling of the terminal capacitances is required. In most existing SiC MOSFET models, the drain-source capacitance is considered meticulously, but it is generally modeled by a junction capacitance of the SiC MOSFET body diode, which would lead to simulation errors. Moreover, most of those models are developed from the data provided in the datasheet. In this paper, an improved SiC MOSFET SPICE model is proposed. The static characteristics and terminal capacitances of the SiC MOSFET from Wolfspeed C2M0080120D are measured for the proposed model. Based on the Enz-Krummenacher-Vittoz (EKV) model, the proposed model utilizes the self-defined channel current to describe the static characteristics, which can simplify the parameter extraction process and reduce the risk of non-convergence. In addition, the drain-source capacitance is studied in detail, an auxiliary capacitance is used for better capacitance fitting and there, an accurate spice model is established. A double pulse test platform is built to test the device. The comparison between simulation and experimental results verified that the improved model is more accurate than previous models.
Interval Observer Design for Uncertain Discrete-Time Polytopic Systems

This paper investigates interval estimation for discrete- time nonlinear systems described by polytopic models with measurable premise variables. By assuming that the disturbances and measurement noise are unknown but bounded, an interval observer design is proposed and sufficient convergence conditions are given by Lyapunov stability analysis. To improve the accuracy of interval estimation in order to attenuate energy-bounded unknown inputs such as disturbances, a direct method based on $H_{\infty}$ approach and pole placement constraints is developed. A numerical example is given to show the effectiveness of the proposed methodoloay.
Back-to-Back H-Bridge Cell Based Modular Solid State Transformer with High-Frequency Link

With the features of control flexibility, multiple functions and compact size, solid state transformer (SST) is considered to be a promising candidate for the energy conversion in applications such as charge station, data center, distribution grid, traction systems and so on. Generally, SST is used to transform medium-voltage AC to low-voltage AC, through the intermediate DC link. Two modular SST topologies based on the back-to-back H-bridge cells with the high-frequency bus as the extra intermediate link are proposed in this paper. Compared with existing modular SST topologies, the two topologies have smaller module counts, thus lower lost and higher efficiency. The working principle of one topology is analyzed as an example, and the corresponding control strategies and design criterion are provided. The feasibility of the proposed topology is verified using the simulation results of a 10kV/380V SST with the capacity of 1MVA.
Shotifier: A Binary Shot Conversion Classifier Pipeline for Football Forwards

In this paper, we present Shotifier, a binary classification pipeline based on the principle of hybrid parallelism. Shotifier pipeline focuses on forwards or strikers, and uses the match statistics, spatial factors like start and end position of the ball, and tactical factors like events, subevents for classifying whether the shot converts into a goal or not (shot conversion). As football is a low scoring game, the emphasis is scoring the winning goal, and due to the unpredictable nature of this game, there are various factors that the clubs’ coach must consider, such as team-formation, weather conditions, lineup, previous results, and many more. However, identifying the critical factors that have a substantial influence on the player’s position for the shot conversion is a challenging task. Hence, in this paper, we focus our research on forwards or strikers to predict the influential factors on shot conversion. Shotifier pipeline is a hybrid parallelism based pipeline that follows a two-step approach: First, applying Kernel Density Estimator to visually represent high concentrated zones on the football pitch for specific events in the match, along with identification of maximum ball activities based on observed ball’s start and end position. Second, using the historical match statistical data to identify the factors that have a strong influence on shot conversion by applying Support Vector Machine, XGBoost, Random Forest, and Multi-layer perceptron models. Finally, we use k-fold cross-validation to evaluate the effectiveness of the models and observe that the precision of XGBoost classifier is 70.39%, which is better than the other models considered in this study.
Synergetic Control of a 3-Φ Buck-Boost Current DC-Link EV Charger Considering Wide Output Range and Irregular Mains Conditions

A wide output voltage range EV charger employing a three-phase (3-Φ) buck-type current source rectifier (CSR)-stage and a series-connected boost-type DC/DC-stage is introduced. The system employs a novel control structure, enabling robust operation even under heavily unbalanced 3-Φ mains conditions. It is verified how the proposed concept achieves PFC operation while regulating the output voltage in a wide range, which requires both buck and boost functionality. This includes a synergetically combined control of the 3-Φ CSR input stage and of the subsequent DC/DC output stage. In this context, a variable DC-link current control strategy referred to as 2/3-PWM is implemented, which allows to switch only two out of the three phases of the CSR-stage. Moreover, seamless transitions between the different operating modes and modulation schemes are demonstrated for different output voltage regions. Finally, the performance of the proposed synergetic control structure is comprehensively validated with closed-loop circuit simulations, focusing on buck-boost operation and on irregular 3-Φ mains conditions, including harmonics distortion, phase loss (phase open-circuit or phase zero-voltage faults), and voltage spikes.
Uncovering Lateral Movement Using Authentication Logs

Network infiltrations due to advanced persistent threats (APTs) have significantly grown in recent years. Their primary objective is to gain unauthorized access to network assets, compromise system and data. APTs are stealthy and remain dormant for an extended period of time, which makes their detection challenging. In this article, we leverage machine learning (ML) to detect hosts in a network that are a target of an APT attack. We evaluate a number of ML classifiers to detect susceptible hosts in the Los Alamos National Lab dataset. We (i) scrutinize graph-based features extracted from host authentication logs, (ii) use feature engineering to reduce dimensionality, (iii) explore balancing the training dataset using over- and under-sampling techniques, (iv) evaluate numerous supervised ML techniques and their ensemble, (v) compare our classification model to the state-of-the-art approaches that leverage the same dataset, and show that our model outperforms them with respect to prediction performance and overhead, and (vi) perturb the attack patterns to study the influence of change in attack frequency and scale on classification performance, and propose a solution for such adversarial behavior.
Tunneling and Resonant Tunneling Effects in the Metal-Ultrathin Oxide-(n+)Silicon Structures

In this work current-voltage characteristics of the experimental metal-utrathin SiO2/highly doped n-type silicon structures are presented and discussed based on theoretical simulations. The measured I-V characteristics exhibit sharp current steps, peaks and hysteresis which are related to tunneling or resonant tunneling through the ultrathin oxide and/or semiconductor substrate energy gap.
Elimination of DC-Link Voltage Ripple in PMSM Drives With a DC-Split-Capacitor Converter

This article presents an electrolytic capacitorless (ECL) motor drive based on a dc-split-capacitor converter. In order to decrease the dc-link voltage ripple of ECL motor drive, an active power decoupling circuit (APDC) with a dc-split-capacitor is proposed. The APDC is composed of two converters, among which the boost converter is named the main converter and the buck–boost converter is named the auxiliary converter. The output capacitors of the two converters are connected in series to increase the dc-link voltage, which expands the speed range of the internal permanent magnet synchronous motor. When there is voltage ripple on the capacitor of the main converter, the auxiliary converter will produce complementary voltage on another capacitor. In addition, an instantaneous current control strategy is proposed in the auxiliary converter to reduce the voltage ripple, while the conventional control strategy is applied in the main converter. The main converter can achieve unity power factor, and the auxiliary converter is used to suppress the dc-link voltage ripple. Experiments are conducted, and the results verify the feasibility and effectiveness of the APDC.
The limitation of the Split-Gate MOSFET(SG-MOSFET) at 3.3kV

Split Gate MOSFET (SG-MOSFET) is one of the effective way to lower the gate-drain capacitance (CGD). However, the applicability of 3.3 kV SiC MOSFET has not been studied yet. In this paper, comparative study of 3.3 kV 4H-SiC SG-MOSFET was confirmed by TCAD simulation compared with conventional MOSFET (C-MOSFET). As a result, the DC figure of merit (DC-FOM) of the two structures are 747.33 MW/cm2 and 605.39 MW/cm2, respectively, and the high frequency figure of merit (HF-FOM) are 754.66 mΩ·pF and 574.5 mΩ·pF. , respectively. Although HF-FOM improved by 24% due to the split gate structure, DC-FOM decreased by 19%. In addition, it does not satisfy the gate oxide reliability and suffered from severe DIBL effect. Therefore, the SG-MOSFET is less effective in improving HF-FOM compared to DC-FOM and causes reliability problems in the 3.3 kV.
Modeling and Integrating Losses of Magnetic Components Into Time-Domain Electric Circuit Simulations

To shorten converter design time and maximize the cost efficiency, advanced design tools are needed. These tools need to be capable of performing simulations in multiple relevant converter domains, such as electric, magnetic, dielectric, and thermal, simultaneously. This paper presents a verified way to calculate semiconductor, magnetic core, and winding losses in a common simulation environment. For validation purposes, a circuit combining a full-bridge LLC resonant converter galvanic-ally isolated through a transformer is simulated. The transformer model is based on electrical and core specifications of a 100 kW, 10 kHz realized medium frequency transformer prototype. The employed transformer model is permeance-based and includes the model of hysteresis losses, evaluated inside the time domain simulations. Thereby, the electrical behavior of the transformer remains preserved with simulated core losses which are consistent with the analytical estimate. Furthermore, the simulated winding losses at rated power match well the measured ones.
Stress Analysis and Characterization of PECVD Oxide/Nitride Multi-Layered Films After Thermal Cycling

Chemically deposited multiple layer films are common structures in microelectronics and MEMS devices and their stress states directly influence both their structural and electrical integrities. From structural reliability perspective, it is important to assess stress state for guiding the structural integrity evaluation. However, the stress states of the film structures during deposition and after post-deposition thermal annealing are process-dependent due to the interaction between thermal stress, void-driven intrinsic stress generation, and stress relaxations. Under such a complex circumstance and multiple length scale nature, it needs an effective model to guide the structural design. In this work, a semi-analytical phenomenological-based model is proposed by simultaneously considering microstructure evolution-dependent intrinsic stress, stress relaxation, and multi-layered curvature-stress relation to predict the stresses existed in each structural layer during thermal processing. The model is then implemented in a finite element model to mimic the entire processing. The model prediction and the experimental results are then mutually correlated and the prediction results agrees with that performed by finite element simulation well. Finally, essential material characterization results in associated with the model prediction are then applied for evaluating residual stresses of each layer in multi-layer nitride/oxide structures as the basis for observed structure failures under indentation loads. In summary, this work provides a rational analysis procedure for stress analysis of multi-layered deposited films during deposition and/or after subsequent thermal processing with experimental verifications and essential demonstrations. This approach should be possible to provide more precise information for optimizing the deposition and the subsequent thermal annealing processes for improving structural longevity of IC devices.
Mosaic: Advancing User Quality of Experience in 360-Degree Video Streaming With Machine Learning

Conventional streaming solutions for streaming 360-degree panoramic videos are inefficient in that they download the entire 360-degree panoramic scene, while the user views only a small sub-part of the scene called the viewport. This can waste over 80% of the network bandwidth. We develop a comprehensive approach called Mosaic that combines a powerful neural network-based viewport prediction with a rate control mechanism that assigns rates to different tiles in the 360-degree frame such that the video quality of experience is optimized subject to a given network capacity. We model the optimization as a multi-choice knapsack problem and solve it using a greedy approach. We also develop an end-to-end testbed using standards-compliant components and provide a comprehensive performance evaluation of Mosaic along with five other streaming techniques – two for conventional adaptive video streaming and three for 360-degree tile-based video streaming. Mosaic outperforms the best of the competitions by as much as 47-191% in terms of average video quality of experience. Simulation-based evaluation as well as subjective user studies further confirm the superiority of the proposed approach.
Input Current Ripple Reduction of Switching Capacitor Converter by Dividing the Output Capacitor

The input current of basic switching capacitor converter is discontinuous and pulsed, hence has large ripple. In this paper, an input current ripple reduction method is proposed by dividing the output capacitor of a dual-phase switching capacitor converter, and repositioning part of the capacitor between the input and output. This method can dramatically reduce the input current ripple without degrading the power density. The simulation result is provided to verify the effectiveness of the proposed method.
Anomaly Detection Between Judicial Text-Based Documents

The problem of searching for anomalies or outliers are extremely important in various fields with problems like fraud detection, crime research, network reliability analysis, medical diagnostics etc.What is an anomaly in the judicial system? A court case is to be considered as an anomaly if the judge’s decision differs significantly from existing decisions in similar cases.In most cases, the existing outlier’s search methods use high-dimensional domains in which data can contain hundreds of dimensions. Such an approach requires lots of resources and clearly is not efficient.Objectives: In this article, the authors:•present two methods (or two models) for searching for anomalies in judicial practice;•give a comparative analysis of the results of the effectiveness of both methods.Methodology: The First method for searching for anomalies is a mix of two models: classification and similarity algorithms. Here algorithms like Logistic regression, Extreme Gradient Boosting (XGBoost), Tensorflow for classification and Latent Dirichlet Allocation (LDA), Latent semantic indexing (LSI) to find similar documents. The Second method shows the usage of the Bidirectional Encoder Representations from Transformers (BERT) embedding model and the Annoy indexing model.Findings: The second method shows better and fast results for searching outliers.Data source: Authors used the set of acts provided by the Supreme Court of the Republic of Kazakhstan. The dataset contains 1 million text documents and metadata.
Topology, Operation Principle and Control of three-line Composite DC Power Flow Controller

The utilization of DC power flow controller in Multi-terminal DC gird can effectively increase the control dimension of DC power flow and improve the active power distribution capability. Based on the research of existing DC power flow controller, a novel Three-line Composite DC Composite Power Flow Controller has been proposed. The proposed topology can realize active power flow control of three individual lines. Its operation principle, control strategy and circuit characteristics are detailed. Simulation results proved that the proposed topology can achieve multi-line power flow control actively.