Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-physics field coupling simulation modeling and analysis of StakPak insulated gate bipolar transistor device

StakPak insulated gate bipolar transistor (IGBT) modules, which have complex internal structures and double-sided heat dissipation characteristics, represent a complex physical environment with multi-physical field coupling under normal working conditions. This makes it difficult to provide an accurate evaluation of the important reliability parameters of the devices contained in these modules, such as the device thermal resistance and junction temperature. In this paper, ANSYS software is used to model and perform a finite element analysis of single-chip StakPak IGBT modules. The heat flux ratio between the collector and emitter of the IGBT chip is calculated via a simulation; the overall power consumption of the module is then divided into two parts, and the thermal resistance values of the two electrodes, the overall temperature and the stress distribution of the IGBT chip and the StakPak device are simulated and calculated. The relationship between the heat dissipation ratio of the two electrodes and the thermal resistance is studied with respect to pressure, temperature and other factors.
Group Leader-Following Consensus Control for Heterogeneous Multi-Agent Systems With Time Delays

In this paper, the problem of group leader-following consensus is discussed for high-order heterogeneous multi-agent systems(HMASs) with multiple leaders and time delays. The four situations are considered: autonomous leaders, active leaders with controllers, and accepting their own information with/without time delays. Different protocols are designed for different situations. Under the corresponding protocols, HMASs can achieve group leader-following consensus without the limitation of in-degree balance. And the networked predictive scheme(NPS) actively compensating for time delays is used to predict current information of agents. Besides, the results are extended to multiple groups case. Finally, the simulation results show that, based on the NPS, the state trajectory of each agent in the time-delay case is very close to that in no time-delay case.
Threshold Switching Enabled Sub-pW-Leakage, Hysteresis-Free Circuits

In this article, we present ultralow leakage logic circuits by combining 3-D memristors with CMOS transistors. Significant leakage current reductions of up to 99% are found by experiments and simulation for a memristive hybrid-inverter if compared with a conventional inverter. Likewise, circuit simulations of memristive hybrid ring oscillators, NAND, or full adders show more than 100% gain in energy efficiency per cycle over state-of-the-art circuits. Importantly, the memristive circuits offer hysteresis-free operation. The hysteresis-free operation is due to properly engineered properties—such as the threshold voltage—of the memristors to match the circuit, as well as the self-adaptive filament diameter of our memristor during operation. Lastly, the memristors feature a 108 ON– OFF ratio, enabling both high speed and low leakage (~10 fA) when integrated with a transistor. They also come with a well-controlled filament formation on a ~10-nm footprint, making them ideal to integrate with modern CMOS technology transistors.
Design of a Wide Output Voltage Dual Active Bridge Converter as Power Electronics Building Block for the Fast Charging of Electric Vehicles

This paper verifies the potential of utilizing the Dual Active Bridge (DAB) converter as a 10 kW Power Electronics Building Block (PEBB) for the charging of Electric Vehicles (EVs) where a wide output voltage range is required. Following the tendency of the new EV charging standards from CCS and CHAdeMO, the commonly employed isolated DC-DC converter should be able to supply the rated power for different vehicles which employ battery technologies which could range from 300 V up to 1000 V. Rectangular (or phase-shift), triangular and trapezoidal modulation strategies are investigated, of which the last two techniques are used for designing the PEBB. Suitable SiC-based semiconductors are chosen, the passive components are designed and the power losses are derived for the whole operating range. It is found that a high power efficiency can be achieved using the designed system.
Single-Phase Transformer-less Photovoltaic Inverter Featuring Low Common-Mode Voltage Noise Generation Enabled by an AC-decoupling and DC-link Mid-point Clamping Technique

This paper studies a single-phase transformerless photovoltaic (PV) inverter featuring AC-decoupling and midpoint DC-link clamping technique which enables a low common-mode voltage noise generation. This circuit is well-suited for PV residential applications. The characteristics of this circuit topology, the principles of operation, and the main dimensioning equations are described in this paper. Finally, simulation and experimental results are used to verify the characteristics of the studied converter.
Gain-Scheduled State Feedback Speed Control of Synchronous Reluctance Motor

In this paper, gain-scheduled state feedback speed controller for synchronous reluctance motor with non-linear inductance characteristics is presented. The novelty of the proposed approach is the combination of state feedback control with gain-scheduling designed for complex plant with non-constant parameters. In order to simplify designing process of controller, feedback decoupling method (FDM) is proposed. An augmented model of the plant is utilized to assure high-performance, steady-state error-free operation of the drive. Linear-quadratic optimization method is employed to calculate optimal non-constant coefficients of controller coefficients. Numerical experiments along with robustness analysis indicate high-performance operation of the considered drive.
Inverse Kinematic solution of u-Rob4 an hybrid exoskeleton for stroke rehabilitation

In rehabilitation of upper-limb disability, robotic exoskeleton often need to perform task-based exercises. In order for exoskeleton to conduct task-based exercises, using inverse kinematic solution is a must to generate the safe trajectory from staring point to end point. Finding inverse kinematic solution for manipulator or exoskeletons that involve hybrid serial-parallel mechanisms always remains an open problem. This situation is worsened when joints with complex motion are considered, such as the shoulder joint. Therefore, in this research we present the inverse kinematic solution of a hybrid exoskeleton called u-Rob4, which includes two parallel configurations to handle the changes of the shoulder center of rotation
A Critical Review on The Effects of Pulse Charging of Li-ion Batteries

In this paper a review on the effects of pulse charging of lithium based battery technology is done. Results published in existing literature are not in complete agreement regarding the effects of pulse charging. Several studies claim to have beneficial effects on charging efficiency, charging time, and capacity fade. While others have found disadvantageous effects on the same parameters. The goal of this paper is to summarize and review these results, based on fundamental theory. Additionally, it will be shown that the electrical equivalent circuit analysis of batteries, often used to explain the beneficial results of pulse charging is an incomplete analogy to fully explain the results of pulse charging.
Predicting Default Risk on Peer-to-Peer Lending Imbalanced Datasets

In the past few years, Peer-to-Peer lending (P2P lending) has grown rapidly in the world. The main idea of P2P lending is disintermediation and removing the intermediaries like banks. For a small business and some individuals without enough credit or credit history, P2P lending is a good way to apply for a loan. However, the fundamental problem of P2P lending is information asymmetry in this model, which may not correctly estimate the default risk of lending. Lenders only determine whether or not to fund the loan by the information provided by borrowers, causing P2P lending data to be imbalanced datasets which contain unequal fully paid and default loans. Imbalanced datasets are quite common in the real worlds, such as credit card fraud in transactions, bad products in the plant and so on. Unfortunately, the imbalanced data are unfriendly to the normal machine learning schemes. In our scenario, models without any adaptive methods would focus on learning the normal repayment. However, the characteristic of the minority class is critical in the loaning business. In this study, we utilize not only several machine learning schemes for predicting the default risk of P2P lending but also re-sampling and cost-sensitive mechanisms to process imbalanced datasets. Furthermore, we use the datasets from Lending Club to validate our proposed scheme. The experiment results show that our proposed scheme can effectively raise the prediction accuracy for default risk.
Interactive PV-Shunt Active Power Filter based on Impedance Source Inverter Controlled by SRF-MVF

This paper deals with an interactive three-phase shunt active power filter (SAPF) connected to a Photovoltaic (PV) source through a Z-source inverter (ZSI). The filter is controlled by synchronous reference frame (SRF) controller based on Multi-variable filter (MVF). The proposed system is designed to reduce the total harmonic distortion (THD) of the source current. In addition, the filter utilizes the PV source to inject active power into the grid as an additional support. The proposed system has been validated through extensive real-time simulation carried out using OPAL-RT-5600 under several scenarios. The conducted investigations are aimed to demonstrate the good performance of the filter in terms of harmonic elimination, operating under weak grid conditions, and injecting active power to the grid.
Birefringence-free tunable terahertz chiral metamaterial

Aiming to explore birefringence-free tunable terahertz chiral metamaterial structure, a new design without sacrificing cell arrangement compactness is proposed. In the current case, a planar metallic spiral structure demonstrating four-fold (C4) rotational symmetry is used. Upon pneumatic actuation induced deformation, it will be translated into a conical quadrifilar helix with a controllable shape. As a result of which, tuning capability over the optical activity and circular dichroism has been successfully achieved from simulation results. Most importantly, besides enantiomeric symmetry, this tuning will not be affected by the in-plane rotation of the linearly polarized incident, demonstrating exciting birefringence-free characteristic.
Inductor AC Resistance Extraction Method from an Impedance Measurement Based on Complex Permeability Model

This paper proposes and evaluates a practical method for extraction and calculation of an inductor AC resistance from an impedance measurement. The method is based on complex permeability model, which mitigates an issue with core loss that can yield to significant errors in the prediction. The experimental measurements are done by a vector network analyzer and core loss estimations, which prove the method versatility to the wide range of wire types and ferrite core sizes.
Research on EMU cable fault diagnosis based on deep belief network and signal domain conversion

In order to realize the intelligent and accurate evaluation of EMU cable health, a cable fault diagnosis method based on signal domain transformation and deep belief network is proposed. Firstly, the collected partial discharge signal is transformed into signal domain after de-noising to realize the visualization of the collected signal and increase the amount of information contained in the signal; then the visualized signal is imported into the deep belief network for evaluation. The test data show that the proposed method can maintain high recognition rate for three typical cable faults, and is superior to other traditional fault diagnosis methods in recognition rate and time-consuming. The diagnosis method based on the combination of deep belief network and partial discharge signal domain conversion has a good engineering application prospect.
Predictive Direct Torque Control of SynRM in Field Weakening Region

In this paper, a finite control set model predictive torque control of synchronous reluctance machine in the field weakening region is proposed. The machine’s future behavior is predicted for a finite set of voltage vectors that the power converter can generate, and then an optimal voltage vector is selected based on a cost function. The cost function is designed considering torque reference tracking and field weakening operation for speeds above rated, while the system constraints are handled as soft constraints and included in the cost function. The proposed control scheme is verified by simulation tests in MATLAB Simulink, and the impact of inductance error on torque reference tracking is investigated. The simulation results have shown the effectiveness of the proposed control scheme. The magnetic saturation effect of the machine is considered and incorporated into the simulations using flux maps.
Design of a Wireless Sensor Node based on MSP430FR5969 for Environment Monitoring Applications

Environment monitoring using Wireless Sensor Networks (WSNs) is considered as a challenging and demanding field, which enables to collect and process large amount of data related to the deployment environment. Thus, the performance of the monitoring system is dependent strongly of the hardware capability of the deployed nodes, as well as of the communication protocols and data transmission strategies. In this work, a new wireless sensor node is introduced. The node uses MSP430FR5969 microcontroller, which offers a high processing capability with reduced energy consumption and has a maximum operating frequency of 16 MHz. The node is compact in size with a dimension of dimensions $53\times 33\ mm^{2}$. The node consumes an average of 27.35 mA during transmission. Current consumption during the sleep and idle modes are 0.33 mA and 31 mA, respectively. An outdoor application is presented, where the node is equipped with a solar panel for energy harvesting. The battery voltage variation and the Received Signal Strength (RSSI) were continuously measured and evaluated.
Consideration of Series-Series and Series-Parallel Topology in Perspective of Dynamic Resonant Inductive Coupling Based Wireless Charging

It is well apparent, compensation is essential in loosely coupled system for efficient power transfer. However, selection of compensating topology in Resonant Inductive Coupling based Wireless Charging System (RIC-WCS) is very much application dependant. Among different existing topologies, Series-Series (SS), Series-Parallel (SP) are deemed to be appropriate and more frequently used in charging scenarios. The choice of these topologies (SS & SP) in perspective of charging conditions under varied receiver coil position in relative to the transmitting coil is evaluated in this article. In addition to this, the implicit relation between Power Transfer Efficiency(PTE) and Power Delivered to Load(PDL) on varying coupling coefficient resulted due to the change in position of the receiving coil under varied frequency have been illustrated through circuit model analysis and theoretical simulation.
A Comparative Study of BLDC Motor Speed Control Using PI and ANN Regulator

This article discusses a comparative study between classical PI control and Artificial Neural Network (ANN) based control of a brushless DC motor. BLDC is recommended for drives that require a large load torque, these types of motors are non-linear systems which require robust control. Due to the saturation characteristic presented by the conventional PI regulator which causes the instability of the system, we have used an artificial neuron-based controller to overcome these aspects and to provide a better dynamic and quick response, in order to control the speed of a BLDC motor as well as the reduction in torque ripples. Simulation results obtained using MATLAB / SIMULINK show that the artificial neuron network controller performance evaluation are better than those obtained by the standard PI controller.
A Prediction Model of CNN-TLSTM for USD/CNY Exchange Rate Prediction

The Exchange rate affects the economic development of various countries. To grasp the information of the changeable exchange rate in time, it is necessary to predict the exchange rate price. This paper proposes the CNN-TLSTM model to predict the United States Dollar/Chinese yuan (USD/CNY) exchange rate closing price of the next trading day. The model consists of two parts, namely convolutional neural networks(CNN)and tanh long short-term memory (TLSTM). The function of CNN is to extract feature factors from the input data. TLSTM is used to receive the output data of CNN for prediction, and finally obtain the prediction result. TLSTM is a new model proposed in this paper to improve the internal structure of the long short-term memory (LSTM). Its advantage is to change the range of the output value of the input gate, retain more data features, and prevent the output value of the input gate in LSTM from being overfitting. This paper selects the exchange rate of USD/CNY data and some stock data for each trading day from January 2, 2006, to October 30, 2020, as the experimental data. To prove the effectiveness of the CNN-TLSTM prediction model, the model is compared with multilayer perceptron (MLP), CNN, recurrent neural network (RNN), LSTM, and CNN-LSTM models. mean absolute percentage error (MAPE), mean square error (MSE), and R-squared (R2) are used for comparative analysis. The experimental results show that the CNN-TLSTM model has the best predictive effect on the USD/CNY exchange rate closing price of the next trading day.
Deanonymization and Traceability of Blockchain-Based Cryptocurrencies: Visual Bit

With the blooming of the Bitcoin market, criminal usages of Bitcoin have become an issue. The pseudonymous design of Bitcoin has brought difficulties to the traceability of transactions. This project develops Visual Bit, a tool that can generate transaction graphs and group joint control addresses to analyze the Bitcoin transaction patterns. Until now, the project has finished implementing the data extraction based on block format and transaction network construction using 5 database tables. However, the program is facing an efficiency problem. The current applied solution is to construct partial networks. Another solution that creates the networks on the cloud is also suggested. Other functions, transaction graph generation and address grouping, will be finished by March.
The Semantics-Syntax Interface: Learning Grammatical Categories and Hierarchical Syntactic Structure Through Semantics

Language is in ﬁ nitely productive because syntax de ﬁ nes dependencies between grammatical categories of words and constituents, so there is interchangeability of these words and constituents within syntactic structures. Previous laboratory-based studies of language learning have shown that complex language structures like hierarchical center embeddings (HCE) are very hard to learn, but these studies tend to simplify the language learning task, omitting semantics and focusing either on learning dependencies between individual words or on acquiring the category membership of those words. We tested whether categories of words and dependencies between these categories and between constituents, could be learned simultaneously in an arti ﬁ cial language with HCE ’ s, when accompanied by scenes illustrating the sentence ’ s intended meaning. Across four experiments, we showed that participants were able to learn the HCE language varying words across categories and category-dependencies, and constituents across constituents-dependencies. They also were able to generalize the learned structure to novel sentences and novel scenes that they had not previously experienced. This simultaneous learning resulting in a productive complex language system, may be a consequence of grounding complex syntax acquisition in semantics.
Environmental and Social Risks to Biodiversity and Ecosystem Health–A Bottom-Up, Resource-Focused Assessment Frame- work

Risks from human intervention in the climate system are raising concerns with respect to individual species and ecosystem health and resiliency. A dominant approach uses global climate models to predict changes in climate in the coming decades and then to downscale this information to assess impacts to plant communities, animal habitats, agricultural and urban ecosystems, and other parts of the Earth’s life system. To achieve robust assessments of the threats to these systems in this top-down, outcome vulnerability approach, however, requires skillful prediction, and representation of changes in regional and local climate processes, which has not yet been satisfactorily achieved. Moreover, threats to biodiversity and ecosystem function, such as from invasive species, are in general, not adequately included in the assessments. We discuss a complementary assessment framework that builds on a bottom-up vulnerability concept that requires the determination of the major human and natural forcings on the environment including extreme events, and the interactions between these forcings. After these forcings and interactions are identified, then the relative risks of each issue can be compared with other risks or forcings in order to adopt optimal mitigation/adaptation strategies. This framework is a more inclusive way of assessing risks, including climate variability and longer-term natural and anthropogenic-driven change, than the outcome vulnerability approach which is mainly based on multi-decadal global and regional climate model predictions. We therefore conclude that the top-down approach alone is outmoded as it is inadequate for robustly assessing risks to biodiversity and ecosystem function. In contrast the bottom-up, integrative approach is feasible and much more in line with the needs of the assessment and conservation community. A key message of our paper is to emphasize the need to consider coupled feedbacks since the Earth is a dynamically interactive system. This should be done not just in the model structure, but also in its application and subsequent analyses. We recognize that the community is moving toward that goal and we urge an accelerated pace.
Costly monitoring in signaling games∗

Off-path beliefs are a key free variable in equilibrium analysis of dynamic games of incomplete information. Our starting point is the observation that, if we take into account players’ incentives to monitor past events in the game, then we can sidestep the question of off-path beliefs. We focus on signaling games where the receiver has to pay a cost to monitor the sender’s action. We show that Nash equilibrium is outcome equivalent to any refinement of perfect Bayesian equilibrium that puts restrictions on the receiver’s off-path beliefs. We then characterize all Nash equilibria that can arise across all cost functions. As an application, we consider the case of vanishing costs to provide a micro-foundation for restrictions on off-path beliefs in standard signaling games where the receiver observes for free the sender’s action.
University–Community Relations in Ghana: Traditional Authority as a Stakeholder1

This article examines the levels and forms of engagement between universities in Ghana and traditional authority. The article is based on an in-depth study of five public universities and their neighbouring communities. Stakeholder theory was used for analysis. Findings from the study indicate that there is a growing awareness among public universities in Ghana of the need to engage the traditional authority of their immediate surrounding communities to enhance university–community relations and coexistence, in order to address the socio-economic and developmental aspirations of the communities and to help achieve the institutional goals of the universities. However, the Kwame Nkrumah University of Science and Technology (KNUST) appears to have a more comprehensive engagement model than the others.