Miracle-dz/UniFAR-FTUs huggingface dataset:
Projections Parameterized Expectations Algorithms (Matlab)

These programs use the techniques described in Ken Judd's 1992 "Journal of Economic Theory" article to solve the standard growth model using parameterized expectations. Another good reference for the solution methods used in these programs is the working paper "Algorithms for Solving Dynamic Models with Occasionally Binding Constraints" by Larry Christiano and Jonas Fisher. All algorithms have the following properties. 1. They use the tensor method to approximate the conditional expectation with orthogonal Chebyshev polynomials. 2. The coefficients of the approximating function are such that they minimize the distance between the approximating function and the numerically calculated conditional expectation at a set of grid points. 3. The grid points are Chebyshev nodes. 4. The numerical integration procedure used to calculate the conditional expectation is Hermite Gaussian Quadrature. In my experience it is easier to obtain an accurate solution fast with quadrature methods than with Monte Carlo methods. 5. The "iterative" programs iterate on a projection procedure to find the coefficients of the approximating function. 6. The "equation-solver" programs use a nonlinear equation solver to find the value of the coefficients at which the approximating function equal the numerically calculated conditional expectation. The included files are: "Iterative" PEA program (peaproi1.m), "Equation-Solver" PEA program (peapro1.m), Procedures to run MATLAB program (newpealib.zip, executable zip file for DOS, Win31, Win 95, Win98, & WinNT), Procedures to run MATLAB program (newpealib.tar, tar-archive for UNIX, decompress using tar -xvf newpealib.tar).
A Novel Control Strategy for Extending the ZVS Range of Triple Active Bridge Converter

Triple Active Bridge (TAB) converter is actively proposed as a multiport DC-DC converter for DC microgrids since it offers high power density, galvanic isolation, and ease of power flow control. In this paper, a novel control scheme is proposed to improve the TAB converter efficiency by extending Zero Voltage Switching (ZVS) range and reducing the circulating current in the transformer windings. By taking into account the controller action during small changes at transient and steady states, controller lookup table design is optimized to ensure a safe margin to keep ZVS and maintain high efficiency. The proposed control strategy is validated using Typhoon Hardware-in-the loop system, and the results are presented.
A Novel Approach of Electrothermal Modeling for Multichip Power Modules

In the thermal evaluation of power electronic systems, the thermal profiles consist of multiple time scales. It is a challenge for thermal model to reduce the computational burden while ensuring the accuracy. This paper proposes a lumped electrothermal model of SiC MOSFET power module, with high computational efficiency and accuracy. The proposed model is based on Cauer network, considering the geometry of power module and lateral heat diffusion of heat sources. The lumped 1-Dimensional (1-D) thermal network of single chip is extracted by Structure Function. The thermal coupling among chips is equalized to power losses injected to particular nodes in Cauer network of each chip. Forward Euler and Mason Formula are used to transfer the obtained network to discrete-time status space equations to enhance the computational efficiency. The proposed model is validated by a finite element analysis (FEA) model, with the error less than 2% under a wide range of time scales. The computational speed increases dramatically, compared with FEA.
Recommending Popular Locations Based on Collected Trajectories

Data gathered from location-aware devices, such as GPS, create opportunities for researchers to extract interesting information from the movement of objects. Popular places are regions that are visited for long durations of time or by a large number of objects. For human trajectories, such popular places are of interest in location recommender systems. In this paper, a number of input trajectories are preprocessed to efficiently answer queries about popular places. Each query specifies one potential popular place and the minimum and maximum duration of a visit. The answer to any such query is the number of visits to the corresponding popular place. We present algorithms for this problem and experimentally evaluate them on real-world data sets. One advantage of the algorithms presented in this paper for location recommender systems is that, unlike most of them, it works even when social network databases are unavailable or unreliable.
Control of Rigid-Soft-Coupled Continuum Arm Driven by Pneumatic Muscles

In recent years, soft robots have become a research hotspot due to their smooth motion characteristics and safe human-robot interaction. Unlike traditional rigid robots, the low stiffness and large deformation kinematic characteristics of soft robots make it relatively difficult to control them accurately. In this paper, a rigid-soft-coupled continuum arm(RSCCA) is designed based on pneumatic muscle drive, which combines the flexibility and safety of a soft robot with the stability of a rigid robot. In series, the RSCCA comprises three modularly designed rigid-soft-coupled joints(RSCJ). We installed joint angle sensors in the rotating joint part of the skeleton, which can monitor the motion state of the skeleton in real-time. Finally, we carried out a closed-loop PID control for the RSCJ based on the joint angle information. The experimental results show that the RSCCA can be closed-loop controlled according to the joint sensing information.
The sound field due to an elliptic cylinder in rectilinear motion

The sound field induced by a cylinder moving perpendicular to its length in an inviscid, compressible fluid is determined. The cylinder has elliptic cross section with semimajor axis a´ and its velocity, characterized by U0, varies on the timescale t0 = a´/U0 and is small compared with c0, the mean sound speed in the fluid. It is also assumed that a´ is very much less than c0t0, a characteristic wavelength, so that the cylinder is compact. By using a modified matching argument, an asymptotic solution for the velocity potential is obtained. In particular, the acoustic field at distances much greater than á is described in terms of moving line source and multipole potentials whose singularities lie at the centre of the cylinder.
Distributed Model Predictive Control of Time-Delay Systems: Application to a Series of Chemical Reactors

In this paper, an approach to distributed model predictive control (MPC) of linear interconnected time-delay systems is proposed. The particular case of distributed control of a series of chemical reactors is considered. The distributed MPC design includes representation of the interconnected time-delay dynamics in the augmented state space and finding a suboptimal solution of the Quadratic Programming problem resulting from the centralized MPC formulation. The proposed approach would be appropriate for embedded distributed MPC of time-delay systems and is illustrated on a simulation example of series of two chemical reactors with recycles.
Multimodal Emotion Recognition Based on Feature Fusion

In the field of human-computer interaction, human emotion recognition is a challenging problem, and it is also a key link to achieve barrier-free communication between human and machine. At present, most of the emotion recognition algorithms are constructed based on single modal social information, and the recognition results are one-sided and easily disturbed. The recognition accuracy is often difficult to meet the practical requirements after being separated from specific social environment conditions. Based on the above situation and problems, this paper adopts multimodal input and simultaneously includes three modal information of audio, text and facial expression to recognition emotion. Three single modal emotion recognition models are proposed based on three different input information, and the multimodal emotion recognition model are constructed by different feature fusion methods. The experimental results showed that the accuracy of multimodal model on the CH-SIMS dataset was 93.92%. In addition, compared with other emotion recognition models, the effectiveness of the proposed method is verified.
Forecasting of PV Generation For Energy Management System in Microgrid Using LSTM

The microgrid is a promote solution for renewable energy systems integration in the grid; it can reduce the penetration limits in the electrical grid due to the intermittency nature of such systems like photovoltaic (PV) power. The ability to forecast the PV power availability for a short-term horizon affects the reliability of the microgrid. Proper forecasting significantly helps to improve the operation of the microgrid. In this work, short-term PV power generation forecasting using long short-term memory (LSTM) recurrent neural network is developed. The impact of changing the LSTM model on forecasting performance was studied. Analysis of forecasting results indicates that when the variation of PV power is higher, it is more complex to deal with the nonlinearity of the forecasting model to provide accurate forecasting.
Local energy flux and subgrid-scale statistics in three-dimensional turbulence

Statistical properties of the subgrid-scale stress tensor, the local energy flux and filtered velocity gradients are analysed in numerical simulations of forced three-dimensional homogeneous turbulence. High Reynolds numbers are achieved by using hyperviscous dissipation. It is found that in the inertial range the subgrid-scale stress tensor and the local energy flux allow simple parametrization based on a tensor eddy viscosity. This parametrization underlines the role that negative skewness of filtered velocity gradients plays in the local energy transfer. It is found that the local energy flux only weakly correlates with the locally averaged energy dissipation rate. This fact reflects basic difficulties of large-eddy simulations of turbulence, namely the possibility of predicting the locally averaged energy dissipation rate through inertial-range quantities such as the local energy flux is limited. Statistical properties of subgrid-scale velocity gradients are systematically studied in an attempt to reveal the mechanism of local energy transfer.
Physical Verification of Data-Driven Cyberattack Detector in Power System: An MTD Approach

Stealthy false data injection attacks (FDIAs) have been shown to compromise power system state estimation. The data-driven detector is a promising way to counter FDIAs. However, it suffers from low interpretability and thus introduces uncontrollable false alarms, which has been overlooked by the literature. This paper proposes to utilise moving target defence (MTD) as an additional physics layer to verify the decisions made by the data-driven detector. First, the data-driven anomaly detector is extended to identify part of the attack vector through iterative normality projection. Second, a novel MTD algorithm is formulated to maintain the high detection rate of the data-driven detector on the identified attack vector with minimum usage. The proposed algorithm is thoroughly tested under the IEEE bus-14 system.
Learn to Grasp Objects with Dexterous Robot Manipulator from Human Demonstration

This study proposed a method to transfer human grasping skills to dexterous robot manipulator. Dynamic motion primitives (DMPs) were used to model the trajectory for the robot manipulator. Users demonstrated a motion task by moving the robot manipulator and thus realizing 3D trajectory. Each joint of the robot manipulator set their own independent DMPs model. This was to enhance the stability of the generated motion towards the target. Furthermore, the generalization of DMPs that the trained model can be applied to similar trajectories, was found and verified in the experiment. A robotic hand (ZealGiant hand) was installed at a 7-DOF robot arm (Kuka iiwa 14), with force sensitive resistance units at the end of the five fingers. A simple data glove was designed to record the human grip posture. A synergistic control was developed for grasping according to the real-time force. Learning from human demonstration for the robotic manipulator developed in study may facilitate programming and mimic a human-like grasping control.
Return power optimization strategy of DAB converter based on model predictive control

Dual active full bridge (DAB) converter produces large return power under traditional phase shift modulation, and its dynamic characteristics are poor. For purpose of solv the above problems, this paper proposes a return power optimization method combining the output voltage model prediction of DAB converter based on EPS modulation and the Lagrange multiplier method. Through in-depth analysis of the mechanism of return power generation, the mathematics model of power of DAB converter is built. The model prediction equation of output voltage of DAB converter is obtained by state space averaging method, and the return power is suppressed by Lagrange multiplier method. Finally, the circuit model is built to verify the analysis conclusion, and these results are compared with those of the traditional SPS modulation and EPS modulation. The results demonstrate that this method can significantly inhibit the return power of the DAB converter, and can effectively improve its output properties.
An Investigation into the Effect of the Gate Drive Resistance on the Performance of the Balanced Inverter

This paper presents an investigation of the effects of the gate drive resistance on the performance of the balanced inverter used for common-mode voltage cancellation. A simplified analytical common-mode voltage model that incorporates the circuit stray inductances is derived to capture the impact of the gate drive resistance on the effectiveness of common-mode voltage cancellation during switching transients. Experiments with different gate drive resistance have been implemented to evaluate the proposed analytical model on a 30-kW hardware demonstrator unit. The experimental results show raising the gate resistance from $4\Omega$ to $8\Omega$ reduces the peak value of the source common-mode voltage frequency spectrum by approx. 6 dB, and the inverter power loss increases by approx. 54%, highlighting the performance tradeoffs associated with this technique.
Face recognition using local binary patterns with image Euclidean distance

Local Binary Pattern (LBP) feature to face recognition has been gaining interest lately. In this paper, a noval face recognition method based on Local Binary Pattern with Image Euclidean Distance(IMED) was proposed. IMED is first embedded in face images. Then a face image is divided into several blocks (facial regions) from which we extract local binary patterns and construct a global feature histogram that represents both the statistics of the facial micro-patterns and their spatial locations. At last, face recognition is performed using a nearest neighbor classifier in the computed feature space with Chi-Squared as a dissimilarity measure. Experiments show that IMED improve the performance of standard LBP algorithm.
Isolated AC/DC converter used in EV/PHEV battery charger from household AC outlet

This paper presents an isolated AC/DC converter used in a battery charger for EV/PHEV. The converter works for EV/PHEV battery charger from a household outlet with AC 100V/200V to the high voltage main battery with 350V. A typical isolated AC/DC converter consists of a PFC circuit and an isolated DC/DC converter. However, this configuration requires the 12-power semiconductor deceives, which is an obstacle to downsizing, cost reduction, and power conversion efficiency. Therefore, the authors integrate an isolated SEPIC circuit and a totem-pole PFC circuit. The number of power semiconductor devices is reduced from 12 to 6. In this paper, the operating principle of the proposed converter is firstly explained. Then, the circuit operation at 1kW output is verified in the experiment.
A Review on Implementation of Vehicle to Everything (V2X): Benefits, Barriers and Measures

Increasing the number of electric vehicles (EVs) is necessary to support decarbonization of energy systems. Although high penetration of EVs brings new challenges to power systems as to provide sufficient energy and capacity for EVs charging, discharging of EVs would be a supportive option for electricity grid operators. Vehicle to everything (V2X) is the capability of EVs to support the grids through the vehicle to grid (V2G), vehicle to home (V2H), vehicle to load (V2L), vehicle to building (V2B) and vehicle to vehicle (V2V) technologies. The V2X technologies accelerate the electrification of the transport sector by creating added values for EVs. This paper provides a review of V2X implementation and the classification of the main benefits, barriers of V2X and possible measures to overcome. An overview of recent V2X demonstration projects worldwide has been presented along with their aims and scopes. Three groups of main benefits (i.e., social acceptance, market benefits, and economic benefits) are identified and discussed. Main barriers for V2X are categorized into four groups (i.e., technical, economic, regulatory, and social) and discussed. The paper also discussed possible measures to overcome the barriers to support successful implementation of V2X.
Solving Two-dimensional Elliptic Singular Perturbation Problems by Particle Swarm Optimization Algorithm

We study a class of elliptic singular perturbation problems on Bakhvalov-Shishkin mesh in this paper. In the process of solving the numerical scheme, we take the error norm between the exact solution and the numerical solution as the objective function, and use particle swarm optimization algorithm to obtain two optimal mesh parameters, so as to obtain the optimal value of error norm. From the results of numerical experimental research, it can be seen that we obtain higher precision numerical solution than selecting mesh transition points manually by the particle swarm optimization algorithm at a low cost, and the numerical solution reflect the singular perturbation characteristics of the exact solutions well.
InfOnto : An ontology for fashion influencer marketing based on Instagram

The present applied research attempts to design an ontology of fashion influencer marketing domain based on fashion marketing resources in Iran that were available during the years 2014-2021. To extract concepts, relationships, and properties with inspiration from the knowledge engineering method proposed by Na and Neoh’s study, Delphi and Domain Analysis approach was used in 3815 influencers’ selection reason documents. To construct the ontology, a combination of The NeOn Methodology and Ontology Development 101, and Protégé (5.5.0 edition), were used. Ultimately, InfOnto was created with 1 conceptual core, 3 main concepts, 81 concepts, 8 categories, 2373 axioms, 1196 logical axioms, 61 object properties, 72 data properties, and 9 annotative properties. Concepts were evaluated using the Delphi method through interviews and questionnaires with 12 experts. In addition to the full supervision of the experts in all stages of the design and construction of the ontology, 30% of the defined concepts and relations were randomly provided to the experts for testing and approval based on their opinion
Accurate and Computationally-Optimized Small-Signal Model Identification of LLC Resonant Converter Based on Machine Learning Techniques

LLC small-signal model is often identified via FHA (First Harmonic Approximation) and/or EDF (Extended Describing Function) in order to analyse the dynamical behaviour of the converter and hopefully fine tune the controller. These approaches fail easily and force to consider a resistive load, which is a restrictive method since most of the times resonant converters are used to feed current or stabilize a voltage (e.g., in battery charging applications). In this paper the small-signal output current response of the converter is approximated by a second order discrete-time transfer function, whose numerator and denominator coefficients change with the operating condition (i.e., output voltage and switching frequency). The coefficients are fitted using a sparse linear combination of functions in data-driven fashion (via simulation) adopting a well-known machine learning operator (Least Absolute Shrinkage and Selection Operator, LASSO). The aim of this paper is to report the first attempts made to obtain an accurate and computationally-optimized approximation of the output current response of a generic resonant converter based on machine learning techniques.
A Novel Power Sharing Strategy for Virtual Oscillator Controlled Microgrid Non-Identical Inverters

Through characteristics analysis, this paper points out there exists mismatch problems of no-load voltage gain and equivalent output impedance between paralleled non-identical inverters in an islanded microgrid. The basic principle of the Hopf oscillator and the inverter control structure based on it are analyzed. Furthermore, a novel power sharing strategy composed of the Hopf oscillator and a synchronous reference frame proportional-integral (SRFPI) controller is proposed to eliminate the characteristics differences of non-identical inverters in terms of no-load voltage gain and equivalent output impedance. To verify the feasibility of the method, a microgrid system with three non-identical inverters connected in parallel is built on the PLECS simulation platform. Theoretical analysis and simulation results show that, compared with the conventional control strategy, the proposed method effectively improves the power equal sharing performance of the paralleled non-identical inverters system.
Short-Circuit Fault Diagnosis of a Three-Phase Current-Source Inverter

The development of the wide-band gap devices has led to the re-emergence of current-source inverters (CSIs). With the modernization of future power systems, the importance of the reliability of power converters is critical. Fault identification is vital to enhancing the reliability of the system. A prominent failure mode in power converters occurs due to the semiconductor device failure. The normal and fault mode operation of the converter is discussed for the three short-circuit fault conditions observed in current-source inverters. This paper proposes a method to detect and locate the short-circuit fault of a MOSFET switch, a diode switch, and a current switch comprising of both MOSFET and diode simultaneously, in a CSI. A fault detection method that involves checking the ratio of the average value of the ac currents and average value of the reference currents in one fundamental cycle to threshold limits is used to detect and identify the location of the fault. This method does not need additional voltage or current sensors for implementing the proposed algorithm. The effectiveness of the proposed method is shown in simulation and experimental results are provided to validate the same. This proposed method for fault identification improves the system reliability of CSIs.