Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Developmental Psychology to Evaluate Artificial Intelligence

Artificial intelligence (AI) systems do not exhibit human-like common sense. The principles and practices of experimental psychology – specifically, work on infant cognition – can be used to develop and test AIs, providing insight into the building blocks of common sense. Here, we describe how the evaluation team for DARPA’s Machine Common Sense program is applying conceptual content, experimental design techniques, and analysis tools used in the field of infant cognitive development to the field of AI evaluation.
SN 1991T: Further Evidence of the Heterogeneous Nature of Type IA Supernovae

Optical spectra of SN 1991T obtained approximately one week before B maximum showed no evidence of the Si, Ca, and S absorption lines which normally dominate the spectra of type Ia supernovae at early phases. Nevertheless, within two weeks after maximum, the spectrum of SN 1991T had evolved to resemble closely that of other type Ia events. These observations suggest that the abundances of Si, Ca, and S in the outer ejecta of SN 1991T were unusually low. The B and V light curves of SN 1991T resembled those of other type Ia supernovae, but the slope of the initial decline following maximum was less steep than normal in both colors
Lifecycle-Based View on Cyber-Physical System Models Using Extended Hidden Markov Models

Many components of Cyber-Physical Systems (CPS) are designed based on models that represent the assumed behavior of the CPS at the time of deployment. However, significant or continuous small changes in the CPS, as well as wear and tear reduce the effectiveness of the CPS and its model and may lead to a total failure of the overall system. In this paper, we propose a novel lifecycle-based view of CPS models. First, we define the model's lifespan as the period from the initial conception of the model until it is no longer fit to represent the system behavior. For better differentiation, a lifespan is divided into the initial, operation, and adaptation phases. In the initial phase, a known-good baseline performance metric is established for the model's suitability to reflect the system behavior. In the operation phase, the model is used for CPS analysis, data smoothing, and fault location while its suitability is monitored. The adaptation phase is intended for necessary adaptations to the model and to the CPS itself, which lead to new iterations. To implement these lifecycle augmentations of the CPS, we use formal modeling in the form of Hidden Markov Models extended by unobservable transitions (Є-HMMT) to represent the assumed system behavior and compare the data of the observed system behavior with this modeling. In addition, we are testing our proposed formalism by designing a CPS model based on smart home systems and running a simulation for validation. The simulation covers unforeseen system changes and corrupted data.
Siamese-Prototypical Network with Data Augmentation Pre-training for Few-shot Medical Image Classification

The main challenge for few-shot medical image classification is that we have a limited number of labeled samples that can be used for training. This may lead to biases in the representation of prototype features, thus affecting the classification performance. In order to solve these problems, a Siamese-Prototypical network with data augmentation pre-training algorithm is proposed for few-shot medical image classification. In this framework, we extend the scarce medical dataset by data augmentation, so that the feature extractor has stronger generalization ability. This feature extractor is then transferred to the meta-learning phase. In the meta-learning phase, we employ a Siamese-Prototypical network to obtain a prototype with higher accuracy. In addition, the supervision information from the support label is used to calibrate the prototype generated from the features of support set, and then use it to predict the samples of support set in the same measure. Our model optimizes three losses, two of the additional losses are dedicated to learning more representative prototypes for more accurate predictions. Experiments on three benchmark medical image classification datasets testify that the proposed method can yield superior accuracy for few-shot medical image classification.
A ZVS based bidirectional Equalizer for Battery Equalization

A soft-switching bidirectional CUK equalizer is presented to balance cells series-connected in the battery string efficiently. The presented equalizer achieves zero-voltage switching (ZVS) and a high efficiency equalization with a resonant tank containing two resonant capacitors, one resonant inductor and two auxiliary switches. The resonant tank in the equalizer reduces switching loss and improves equalization efficiency compared with hard-switching equalizers. The topology and operating principle of the presented equalizer are given in this paper. An experimental platform is designed and experimental results show that the efficiency increases around 2% -3% compared with a zero current switching equalizer at 300kHz switching frequency.
Online Pressure Measurement in the Ski Boot to Analyze the Carving Technique

This paper describes the design of an online pressure measurement system to determine the correct load for the carving technique in skiing. For this purpose the state of the art for insole pressure measurement, skiing analysis and mobile data acquisition devices is being investigated. The research shows that there is no pressure measurement system available yet that uses pressure sensors on the lower leg to make statements about the lower extremity movements during skiing. Therefore, a pressure measurement system was developed, which uses eight sensors under the soles of the feet and on the lower legs and transmits the collected data via Bluetooth Low Energy (BLE) to a cell phone. The measurement data is displayed and stored for further analysis with the phyphox application. With the help of the measurement system it is possible to make statements about incorrect posture and typical movement sequences during carving and to obtain reproducible results. In the outlook, optimization proposals for further research are presented.
Adaptive Resonant Controller Based SOGI-FLL for Three-Phase Voltage Source Inverters

The proportional resonant (PR) controller is a real alternative to the standard proportional integral (PI) controller referring to its effectiveness and superior performance when controlling sinusoidal waveforms and its capacity to compensate for low order harmonics using the harmonic compensator (HC). The resonant frequency value is required for both PR and HC's internal models. The nominal value of the grid frequency and its multiples are employed in most cases, however, when the grid frequency fluctuates, the performance of both PR and HC suffers. This work proposes an adaptive proportional resonant (PR) controller for a three-phase grid-connected inverter this controller can adapt its control parameters during the grid frequency fluctuations based on the information issued by the SOGI-FLL, this concept helps to enhance the behavior of resonant controller and harmonic compensator in the case of grid frequency deviations. Further, the effectiveness and robustness of the suggested adaptive resonant controller for grid-tied inverters are clearly demonstrated by simulation results using the matlab environment.
Advanced Self-Oscillating Control for Domino Wireless Power Transfer Systems with Quasi-Load-Independent Outputs

Conventional self-oscillating control is designed for two-stage wireless power transfer (WPT) systems with load-independent (LI) outputs. In this paper, an advanced self-oscillating control is proposed for domino WPT systems by considering the effects of equivalent series resistances (ESRs) of the resonators on output performance. Due to the existence of ESRs, quasi load independent (QLI) output voltage is achieved by the proposed control. The optimal operating frequency, which results in the minimum output voltage deviations from the references, can be automatically selected by the proposed control. A four-coil series-series (SS)-compensated domino WPT system is set up to validate the effectiveness of the proposed control in practice.
A Series-parallel Connected Four-transformer LLC Converter Based on Series-connected IGBTs for Medium-voltage High-power Applications

DC power distribution system has become an important development trend of urban power distribution network due to its advantages of high power density, high conversion efficiency and flexible networking. Medium-voltage high-power solid-state transformer play a more crucial role in the interconnection of medium-low voltage DC systems. In this paper, a series-parallel connected four-transformer LLC resonant converter based on series-connected IGBTs is designed. A half-bridge module with six IGBTs in series is used on the switch network side to improve the voltage stress tolerance level of the switch network and increase the power density of the system. The voltage and current stress on the primary and secondary sides of the transformer can be reduced at the same time by combining four transformers in series and parallel, which is convenient for the optimal design of the transformer, and the output power of the four transformers can be naturally balanced.
Evaluating CodeClusters for Effectively Providing Feedback on Code Submissions

Full research paper—Most introductory programming courses rely on the use of automated assessment for grading programming assignments. While such systems save teachers’ time by eliminating manual grading, the submissions may not be reviewed by the teachers at all, losing valuable insight into how students solve the assignments. In this paper, we introduce CodeClusters which provides teachers’ a quick overview of general patterns in code submissions. The main features of the system are full-text search and N-gram -based similarity detection model that can cluster and subset the code by various aspects such as AST similarity or software also has It has also an interface for streamlining the writing of feedback to multiple submissions at once. CodeClusters has been primarily designed to be used jointly with automated assessment systems where automated tests would assess the functionality of the code, and CodeClusters would be used for gaining a higher-level view of programming patterns and for writing feedback to students. CodeClusters was evaluated in a think-aloud study with university lecturers responsible for programming courses at two research-first universities. The lecturers were pleased by the possibility of providing better feedback to students quickly, saw it could improve the quality of their courses over solely automated assessment, and expressed interest in using CodeClusters in their own courses.
Modeling and Design Method of Coupled Inductor Using Powder Core with Concentrated Air Gap

Concentrated air gap is utilized to suppress soft saturation characteristics of powder cores by increasing effective magnetic reluctance. This paper proposes a high-precision magnetic circuit model for powder core in coupled inductors. The proposed model, considering soft saturation and flux cancellation, can be utilized even with large air gap, and simplifies the magnetic design. The model features good accuracy of inductance variation in a wide range of winding current. Five prototype coupled inductors were built using the proposed model and applied to a 1-kW interleaved boost converter to prove the precision of the proposed magnetic circuit.
Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer

Particle swarm optimizationPSOis a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizerCLPSO� , which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.
Cross-Interference Free 6.78 MHz Multiple-Transmitter Using Power Factor Based Control for Wide-Area Wireless Power Transfer Systems

Simultaneous wireless powering of mobile devices and home appliances over a wide area using multiple transmitters operating at 6.78 MHz is an attractive application of resonant inductive coupling wireless power transfer. Nevertheless, this application often suffers from two types of cross-interference. The first is cross-interference among the transmitters, which may cause the hard-switching of the inverter within the transmitter. The second is cross-interference among the receivers via the transmitter, which may not only cause the output power reduction but also complicate the power distribution control for the receivers. To solve the two cross-interference, this paper proposes a novel 6.78 MHz multiple-transmitter. The proposed multiple-transmitter adopts a simple active variable reactance component called an automatic tuning assist circuit (ATAC) for each transmitter. The ATAC works to fix the phase of each transmitter current regardless of the magnetic coupling among the transmitter, eliminating the cross- interference among the transmitters. Furthermore, the proposed multiple-transmitter also incorporates inverters with power factor based control effectively utilizing the transmitter current phase fixed by the ATAC. This inverter controls each transmitter current amplitude to be constant regardless of the receiver, removing the cross-interference among the receivers via the transmitter. The effectiveness of the proposed multiple- transmitter was verified by experiments.
Loss Estimation of a Dual Active Bridge as part of a Solid State Transformer using Frequency Domain Modelling

This paper proposes an improved method for the loss estimation of a Dual Active Bridge (DAB) using frequency domain modelling. The method uses a detailed, frequency depending transformer model to describe even highly utilized DABs. The model is used to estimate the occurring losses of a DAB as part of an modular Solid State Transformer (SST). The influence of the SST's phase power ripple is considered for the loss estimation. The results of the frequency domain model and the loss calculation are validated using measurement data of an SST-cell prototype.
Post-Impact Control to Mitigate the Secondary Collision by Combining LQR with Feed-Forward Control

Road traffic statistics shows that multi-event collision accidents cause significant losses to the society. The impact forces often cause very large heading angle and lateral deviation, leading to more severe secondary collision and even fatal crashes. This paper propose a post-impact controller based on an active front axle steering actuator. It can attenuate the vehicle’s undesired motion deviation after the first impact. The controller, which integrates the path tracking error model with vehicle dynamics model, is developed by fusing feed-forward control and with linear-quadratic regulator (LQR). We use Carsim and Simulink co-simulation to validate its effectiveness on both straight and bend road, the latter of which has not been studied in the literature. We also build a high-performance scaled vehicle platform for future experimental investigation of our and other post-impact control strategies.
Analysis of UAVs' Coverage Efficiency in Flight and Static Mode

Nowadays one of the critical tasks for analyzing the usage of unmanned aerial vehicle (UAV) as base stations is the development of effective UAV control strategies while servicing customers one the ground. The main problem is states within ultimate battery capacity, which limits the UAV's flight's duration and battery lifetime. This paper presents a scenario for placing UAVs on stationary objects, such as roofs, walls or balconies of buildings, in the process of providing communication services. Such a strategy provides drones to extend their autonomous work during the flight in the customer service area, as the main source of power consumption, the engine, can be turned off for long periods of time after arriving in a subscriber's service area. The paper compares the proposed scenario with the standard flight mode of UAVs.
Small-Signal Stability Support From Dynamically Configurable Grid-Forming/Following Inverters for Distribution Systems

The growth of inverter-based resources (IBRs) in modern power systems can challenge system stability since they do not natively provide inertia, prompting a need to develop new methods to address the potential instabilities. In this paper, a dynamically configurable grid-forming (GFM) and grid-following (GFL) control is proposed, which enables inverters to flexibly transition to support system needs. The operation mode transition between GFM and GFL controls is adopted as an additional degree of control flexibility to adjust the small-signal stability margin. In addition, a holistic and detailed small-signal model for a distribution feeder that captures the dynamics from both synchronous generators (SGs) and IBRs is also derived and used to evaluate the effectiveness of the proposed GFM/GFL controls to augment the system small-signal stability margin. Case studies with varying levels of IBRs were implemented in MATLAB/Simulink to demonstrate the effectiveness of the proposed approach.
3D-FERNet: A Facial Expression Recognition Network utilizing 3D information

In this paper, we propose a 3D information-based facial expression recognition network (3D-FERNet), which effectively combines identity, 3D and 2D expression information for facial expression recognition (FER). The 3DFERNet model consists of three feature encoders to extract identity, 2d expression, and 3d expression features, and one semantic-based feature fusion module to combine these features for further classification. Firstly, the identity encoder is constructed by a face identity recognition network, which is used to perform identity-conditional FER and remove identity bias of FER prediction results. Secondly, we design the 3D expression encoder by a 3D face reconstruction network, which is trained under a semi-supervised method with a variety of unlabeled expression data. The generated 3D features can capture the subtle differences between similar expression cases. Thirdly, for the 2D expression encoder, we can leverage most existing FER networks as the backbone. Finally, after obtaining these facial features, we propose a semantic-based feature fusion module, which is based on the attention mechanism for feature combination. Experiments show that the proposed 3D-FERNet achieves state-of-the-art classification accuracy in multiple benchmark data sets. Moreover, the identity feature encoder and 3D feature encoder can serve as two general modules to plug in most existing FER models.
A Study on Stator Shape to Reduce Cogging Torque and Torque Ripple of Double-Layer Spoke Type PMSM

The spoke type permanent magnet synchronous motor (PMSM), which is a general ferrite magnetic flux concentrated motor, has a low portion of reluctance torque at the total torque magnitude. Therefore, as a way to increase the reluctance torque, there is a double-layer spoke type PMSM that can maximize the difference in inductance between the d-axis and the q-axis. However, in the double-layer spoke type PMSM, cogging torque, torque ripple, and total harmonic distortion (THD) of Load Line EMF increase as reluctance torque is maximized, which is the main cause of vibration and noise. As a representative method, it can be solved by applying skew to the rotor, but the conventional method of applying skew is not a good method in terms of productivity because it is difficult to manufacture because it divides the stages of the permanent magnet. Therefore, in this paper, the skew is not applied to the rotor and the asymmetrical design of the shoe is carried out in the stator. Then, we propose a new stator shape that can apply a Skew-like effect by cross-stacking stators using the asymmetric designed stator. Based on the method, the reduction of cogging torque and THD was verified by Finite Element Analysis (FEA).
Optimal Siting and Sizing of Energy Storage Power Station Considering Peak Regulation Adequacy

With the rapid development of wind power and photovoltaic power generation, the lack of flexibility in peak regulation further affects the new energy consumption. In order to alleviate the peak regulation pressure of thermal power units, a comprehensive evaluation index of peak regulation adequacy and an energy storage power station planning method of high-voltage distribution system are proposed in this paper. The development margin of new energy and the growth of load during the planning period are taken into account. Both the economics of energy storage peak regulation and the adequacy of source-storage coordinated peak regulation are considered. The effectiveness of the proposed optimal method for energy storage power station siting and sizing has been verified in the modified IEEE 30-bus power system.
Discrete-Time Model of Dual Active Bridge Converters Considering Digital Controller Delay

This paper proposes a discrete time model for dual active bridge (DAB) converters by taking the delay of micro-controller units (MCUs) into account based on the subsampling mode. The proposed model addresses the issue of traditional discrete time model which cannot accurately reflects the delay effects on dynamics introduced by calculation and phase shift modulation. The proposed model is compared with various existing small-signal models including reduced order model, generalized averaging model, traditional discrete time model, and the accuracy is verified through computer simulation.
Estimating the Common Mean of Possibly Different Normal Populations: A Simulation Study

Abstract Relative efficiency of estimators of the common mean of possibly different normal populations N(μ, σ i 2) is investigated empirically. A weighted least squares estimator , with weights based on a modification of minimum norm quadratic unbiased (MINQU) estimators of the σ i 2, is found to be substantially more efficient than the maximum likelihood (ML) estimator of μ when the heterogeneity in the σ i , is small to moderate and the number of sample observations from a population is small. The jackknife t statistic for performed well in regard to both coverage probability and expected length of the confidence interval.
You Only Look & Listen Once: Towards Fast and Accurate Visual Grounding

Visual Grounding (VG) aims to locate the most relevant region in an image, based on a flexible natural language query but not a pre-defined label, thus it can be a useful technique in practice. Most methods in VG operate in a two-stage manner, wherein the first stage an object detector is adopted to generate a set of object proposals from the input image and the second stage is simply formulated as a cross-modal matching problem. There might be hundreds of proposals produced in the first stage that need to be compared in the second stage, which is infeasible for real-time VG applications, and the performance of the second stage may be affected by the first stage. In this paper, we propose a much more elegant one-stage detection based method that joints the region proposal and matching stage as a single detection network. The detection is conditioned on the input query with a stack of novel Relation-to-Attention modules that transform the image-to-query relationship to a relation map, which is used to predict the bounding box directly without proposing large numbers of useless region proposals. During the inference, our approach is about 20 x ~ 30 x faster than previous methods and, remarkably, it achieves comparable performance on several benchmark datasets.