Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Three-Terminal Model for PWM Converters with Current-Mode Control

The two nonlinear components in a PWM converter are power switches and PWM modulator. In a voltage-mode control, they were modeled separately due to the sufficient low-pass filtering from both power stage and feedback compensator. In more advanced controls such as peak current-mode, constant on-time, or $\mathrm{v}^{2}$ controls, more state variables are fed-back into the control loop with less or no filtering in order to achieve a better dynamic performance. Consequently, power stage and feedback control must be considered as one entity to properly account for contribution from harmonics. J. Li derived the corresponding models based on describing function method, and the mathematical model was then approximated with an equivalent-circuit form by Y. Yan. However, the perspective of the current feedback was lost since power stage and feedback were lumped together. Moreover, the average-model based power stage cannot address the contribution from harmonics. This paper proposes an equivalent circuit model that preserves the current feedback information, and is accurate at half switching frequency at all terminals due to the improved power stage model.
An Assessment of Linear and Nonlinear Features for Detecting Olfactory Stimulus in EEG

The sense of smell is one of the oldest senses of humankind and is able to provide valuable information from the mood of a person to purchase intention. In this study, five non-linear features; 3 Hjorth Parameters namely, activity, complexity, and mobility, Higuchi's Fractal Dimension, and Lempel-Ziv Complexity were used to differentiate EEG signals of participants with or without being subjected to olfactory stimuli using several machine learning methods. Experimental results were compared to our previous study where classification was performed using EEG sub-band powers. It was concluded that non-linear features were superior in differentiating olfactory stimuli, especially for frontal, temporal, and occipital channels.
Tests of a ladder of density functionals for bulk solids and surfaces

The local spin-density approximation (LSDA) and the generalized gradient approximation (GGA) of Perdew, Burke, and Ernzerhof (PBE) are fully non-empirical realizations of the first two rungs of "Jacob's ladder" of exchange-correlation density functionals. The recently proposed non-empirical meta-GGA of Tao, Perdew, Staroverov, and Scuseria (TPSS), featuring the kinetic energy density as an additional local ingredient, completes the third rung. A hierarchy of these functionals, complemented by the meta-GGA of Perdew, Kurth, Zupan, and Blaha (PKZB), is tested in self-consistent Gaussian-type orbital calculations of equilibrium lattice constants, bulk moduli, and cohesive energies for 18 solids, and in studies of the jellium surface energy. The ascent of the ladder generally results in better performance, although most of the improvement for bulk solids occurs in the transition from LSDA to PBE. For the jellium surface energy, PBE is less accurate than LSDA, but PKZB and TPSS are more accurate. We support the idea that most of the error of these functionals for bulk solids arises in the description of core-valence interaction, by demonstrating that it can be removed through adjustment of the corresponding term in the equation of state. Overall, TPSS gives the best description of solids and surfaces, as it was found to do for molecules in earlier work.
Face Value: On the Impact of Annotation (In-)Consistencies and Label Ambiguity in Facial Data on Emotion Recognition

Artificial Intelligence (AI)-based emotion recognition using various kinds of data has attracted vast attention in recent years. Impressive results have been achieved, but only recently the influence of the training data with its potential biases and variations in annotation quality are discussed. Still, the majority of the research literature focuses on improving machine learning techniques and model performance using single data sets. Literature on the impact of training data remains scarce. Therefore, in this paper we investigate the influence of the training data on the accuracy of recognizing emotional states in facial expressions by a comparative evaluation, using multiple established facial image databases. Results reveal inconsistencies in the data annotations as well as ambiguities in the emotional states expressed. Thus, they allow to critically discuss data quality of the training data, contributing to a more in-depth understanding of previous emotion recognition approaches, and improving the design of more transparent AI solutions.
Further development of a hybrid‐isentropic GCM

The UK Universities' Global Atmospheric Modelling Programme hybrid‐isentropic general‐circulation model (HIGCM) uses a flexible σ‐θ;‐p vertical coordinate, where p is pressure, θ is potential temperature, and σ = p/p* where p* is surface pressure. Three major improvements to the HIGCM are presented. the first improvement is a modification to the vertical‐difference scheme so that spurious vertical motions in the isentropic domain are minimized. the second improvement is a modification to the implementation of the radiation scheme so that it is now able to damp, and does not itself create, noisy temperature profiles; this allows the model to be run without ad hoc extra vertical diffusion and so allows a cleaner comparison with σ‐p simulations. the third improvement is to extend the isentropic domain up to the top of the model thus allowing σ‐θ or σ‐θ‐p simulations to be performed.
Takagi–Sugeno fuzzy-based incremental conductance algorithm for maximum power point tracking of a photovoltaic generating system

Sluggish tracking for change in solar irradiations is the main demerit of the perturb and observe (P&O) algorithm because of its fixed perturb. To overcome this, an adaptive tracking algorithm based on Takagi–Sugeno fuzzy implications is proposed in this study. Input to the fuzzy controller is the error between conductance and incremental conductance which is otherwise zero at the maximum power point. The P&O algorithm and the proposed algorithm along with the recently published adaptive incremental conductance algorithm are examined for their performance efficacy on a photovoltaic (PV) generating system with fabricated as well as real irradiation data. As a case study, all the considered algorithms are validated under partial shading conditions also. The effectiveness of the proposed algorithm is verified for the tracking of the maximum power point of a PV system in steady as well as changing irradiations and the conclusions are supported through some experimental validations.
EXPLOSIVELY HEATED GAUSSIAN OBJECTS *

The hydrodynamic expansion of Gaussian-shaped objects subject to uniform rapid heating was exactly calculated using the similarity technique. Such explosions were found to be characterized by only two energy parameters. The dependence of hydrodynamic quantities on the heating rate and deposited energy was determined. Finally, we compared the similarity solutions to a numerical solution for a square object explosion and found good agreement is reached by a time t/sub p/ and continues thereafter. Apparently, the natural shape for an exploding object is a Gaussian density profile. It will asymptotically approach this profile regardless of its initial shape.
Scene Aware Semantic Crack Segmentation from Oblique Drone Imagery

Cracks are one of the earliest indications of structure degradation relevant to buildings, roads, or widgets. How-ever, false alarms and misjudgments are easy to occur when segmenting cracks from large scenes. Thus, robustly detecting and precisely segmenting non-salient cracks are always difficult, especially for large oblique drone images. To handle the problems above, we propose a novel scene-aware semantic crack segmentation model, M-CrackNet. The contributions of the proposed algorithm can be mainly concluded as: (1) it decomposes the task of building crack detection into crack related scene activation and semantic crack segmentation so that the method can be transferred to other scenes with cracks; (2) by parallelly processing images with two decomposed modules, the computational cost has been largely reduced. Meanwhile, most of the false alarms and misjudgments are avoided, enhancing the accuracy of semantic crack segmentation. The effectiveness of the proposed method is evaluated upon CPRI, CrackForest, and the CrackODI proposed in this paper. Compared with the state-of-the-art methods, the mIoU and BFScore of the M-CrackNet are improved by 0.64%∼8.01% and 3.12%∼33.29% on datasets, which demonstrate that the M-CrackNet is effective for semantic crack segmentation.
A Comparative Trajectory Tracking Controls of a Mobile Robot

The work presented in this paper is focused on developing robust control laws to solve the problem of traj ectories tracking of differential wheeled mobile robots pioneer 3DX. The main objective is to guarantee the correct behavior of the system and to ensure an accurate traj ectory tracking under the system parametric variations. The designed control law forces the robot to perform minimum error path tracking in order to follow a certain path to reach the final destination in a predefined time.
PeQA: A Massive Persian Question-Answering and Chatbot Dataset

TA question-answering (QA) system is an application able to communicate with humans using natural language processing. Modelling a dialogue between humans and machines is considered one of the most important tasks of Artificial Intelligence (AI). Creating a Chatbot with a good performance in modelling human-machine conversations is still one of the unsolved challenges in this field. Although Chatbots have many applications, in general, they should understand users' meaning through their words and provide them with relevant answers. In the past, Chatbot architectures mainly relied on rules or statistical methods. With the advent of deep learning methods, trainable neural networks soon replaced the traditional models. These sorts of deep models are highly affected by the dataset that would be fed into them, and there is no big enough one available in the Persian language! We present a huge dataset of 14 million Persian tweets from tweeter that is meticulously processed to create a rich collection of 420,000 pairs of question-answer data. We also present modelling results on Transformers, including Sensibleness and Specificity Average (SSA) and the BLEU metric. We will release our dataset, modelling code, and models publicly 1.1https://github.com/mut-deep/PEQA
Phase Retrieval: Application to Audio Signal Reconstruction

Theoretically, phase retrieval is an efficient method for signal reconstruction given only the magnitude spectrum of the short-term Fourier transform (STFT). This topic has recently regained popularity due to its utility in a variety of applications such as compressive sensing, speech synthesis, speech enhancement, source separation, and so on. As a result, based on an explicit relationship between STFT magnitude and phase, this paper presents an efficient algorithm for audio signal reconstruction using phase retrieval from the STFT magnitude spectrum. The standard metrics in signal reconstruction, such as time domain segmental signal-to-noise ratio (segSNR), time-frequency domain signal-to-error ratio (SER), and cepstrum-related distance measures, such as log-likelihood ratio (LLR), Itakura-Saito distorsion (IS), and cepstrum distance, are used to perform an objective evaluation. The results support the proposed approach's improvement.
A Computationally Efficient FCS-MPC Imitator for Grid-Tied Three-Level NPC Power Converters Based on Sequential Artificial Neural Network

Finite control set model predictive control (FCS-MPC) has attracted lots of attention recently thanks to its intuitive design and ease to include multiple control objectives and constraints. However, its computational demand surges exponentially with the number of independent switching pairs and the prediction horizons. To reduce the computational burden, we propose a fast FCS-MPC imitator based on two sequential shallow artificial neural networks (SANN). Compared with the existing ANN based strategies, the proposed solution has much reduced number of neurons in hidden layers, and is much suitable for redundant voltage vector selection. The proposed solution is tested with a grid-tied three-level neutral point clamped power converter (3L-NPC). Simulation results confirm that, i) the computational complexity is much reduced than both FCS-MPC and ANN-MPC while achieving similarly qualified control performance, ii) SANN-MPC shows better imitation accuracy than the classical ANN based MPC.
Towards Sentiment and Emotion aided Intent Detection

Intent detection is one of the crucial Natural Language Understanding(NLU) tasks studied extensively. Misclassification of intents impacts the overall performance of the conversational systems as natural language understanding is the first mean of interaction between a user and a virtual agent. The traditional approach of intent detection is limited only to textual features of user utterances and overlooks other semantic features. The sentiment and emotional state of the speaker are two such semantic features that have essential impacts on intent detection, as they implicitly express user intention conveyed through the user’s message. Depending on the context, these features can help the dialogue agent respond to the same intent with varying degrees of sentiment and emotion. Thus, investigating the role of sentiment and emotion on intent detection is a matter of great interest. The current work investigates the impact of utilizing sentiment and emotion information on intent detection tasks and proposes emotion and sentiment aided intent detection models. We also investigate the impact of sentiment and emotion using three different multitasking frameworks and present a joint model that utilizes the co-relation information across these tasks to correctly identify all these NLU aspects (intent, sentiment, and emotion). The obtained experimental results by the proposed models outperform several baselines and state-of-the-art intent detection models on multiple datasets by a significant margin of 1.8% - 3%, demonstrating the significant role of sentiment and emotion features in intent detection1.
Weakly Supervised Learning in a Group of Learners with Communication

In the initial research, we executed these inferences according to the limited graph that we used as input, although the approximate inference can also be used. We have designed a system where learners with little knowledge of the problem can label unlabeled data with each other’s help and this data can be used to train learners. It should be noted that sharing opinions It causes solidarity. In our proposed method, the accuracy of the system is controlled by preventing the correlation effect between users in voting for labeling unlabeled data.
NEWSFARM: A Large-Scale Chinese Corpus of Long News Summarization

Recently, the field of natural language processing (NLP) has grown rapidly, driven by massive datasets. At the same time, the need for automatic summarization systems has been rapidly increasing as the amount of textual information on the web and in large data centers became intractable for human readers. However, the lack of large-scale and high-quality Chinese datasets remain a critical bottleneck for further research on automatic text summarization. To close this gap, we searched domestic and foreign Chinese news websites and designed the FCFS (Format and Content Filtering System) to crawl and filter these records to construct NEWSFARM. The NEWSFARM is a large-scale Chinese long news summarization corpus, containing more than 220K Chinese long news and summaries written by professional editors or authors, all of which are released to the public1. We calculated the static metrics and designed many experiments with the baseline models to evaluate the dataset. By comparing with the common datasets, the results not only demonstrate the usefulness and challenges of the proposed corpus for automatic text summarization but also validate the superiority of FCFS.
Sharing frequency containment reserves with HVDC systems

A transmission system operator requires frequency control ancillary services to ensure reliable and secure power system operation. With the increasing share of renewable generation and a decrease in the traditional resources for these services, it is crucial that the system operators can share these resources across different electricity markets in Europe. The increasing number of HVDC interconnections provides an attractive option for sharing these reserves. This paper presents a security-constrained optimal power flow based model to investigate the benefits of using HVDC systems for frequency containment reserves procurement. Different types of frequency containment reserves in the Nordic synchronous area are considered in the model. The proposed model is used on a representative test system using publicly available data to show that the use of the HVDC system results in a decrease in the cost of reserve procurement and thus leads to an increase in social welfare. The analysis shows that the availability of HVDC system capacity and reserve price influence the procurement decisions.
Multi-Objective Optimisation of Urban Design Using a Genetic Algorithm

In the current fast-growing cities, urban planners are continually required to optimize city designs to improve the urban environment whilst meeting ever more stringent regulations. Thus, the demand for optimization is increased continuously. However, real-world optimization problems often have a large search space and have objective functions with many conflicting objectives that are expensive to evaluate. The decision-making process to select a solution is equally demanding. These difficulties could be relieved by tools supporting the urban planners and decision-makers throughout the entire process of urban planning. This paper aims to show the potential and share insight gained using a genetic algorithm in such a problem area. It proposes an approach for parametric urban design at a neighbourhood level that optimizes the buildings’ ground floor area (GFA) and green area in relation to the demography of the population. The approach is based on dynamic parameters such as demography, and urban morphology, which are linked to people’s needs rather than fixed rules. It is automated in a tool implemented using a nondominated sorting genetic algorithm (NSGA-II). The tool proves to be useful for urban planning when decisions influencing the development of the city for a long time are taken.
Mapping vector accumulator: fractal domain feature for character recognition

A new feature is presented which is calculated directly from the fractal-compressed form of images. The performance of this feature is analysed by using it in the training of standard classifiers for a character recognition problem. The feature is shown to be translation invariant, giving it an advantage over methods that use pixels or compressed data directly.
Optimal Allocation of Distributed Energy Storage Systems in Weak Grid: A Case Study of Ali, Tibet

Nowadays, integrating more renewable energy into power systems has been viewed as an irreversible route to achieving carbon neutrality. However, with the trend of high renewable energy penetration, the randomness and volatility pose a great challenge to the power grid. Compared with the power system with strong inertia, the weak grid is more vulnerable to fluctuations caused by renewable energy generation. In this sense, distributed energy storage systems are necessary for a weak grid to mitigate the negative effect of renewable energy. This paper proposes an optimal location and sizing framework for distributed energy storage systems in a weak power system. Furthermore, the problem is formulated as a non-linear, non-convex optimization problem and solved by an adaptive particle swarm optimization. A real-world weak grid, Ali regional power grid, is selected to perform case studies and numerical results demonstrate the efficiency of the proposed method.
Numerical modeling of electrical/optical combination for the simulation of PIN photodiode

PIN photodiode has emerged as the most promising technology for optical device design. The analyze of their optical and electrical performances versus the technological parameters is so important. We present in this paper an efficient numerical model of the PIN photodiode based on electromagnetic description of the optical carrier generation with drift-diffusion model self- consistent solving. The proposed numerical simulations is based on a finite-difference estimation of drift-diffusion model (DDM), which formed by Poisson's equation and the carrier transport equations. In order to improve the convergence of the proposed model, Gummel’ s scheme was used to handle the active device model coupling. The technical parameters performance values of the of light sensitive PIN diode are improved by introducing the optical power distribution inner the PIN diode by taking in consideration geometric and optical characteristics, like the microlens and absorbing regions. Numerical results are generated and discussed such as the space distribution of the electrostatic potential, electrons concentration, terminal current for different power intensity and finally spectral sensitivity. The results show that the PIN photodiode has a good sensitivity for a large optical wavelength. Spectral results are compared with commercial simulator (SIL V ACO-TCAD) and also with similar papers. According to this comparison, the proposed model gives close and logical results.
A Novel Atomic Magnetometer Laser Source Control System for Magnetic Measurement

In order to provide an accurate emission light source to the atomic magnetometer for magnetocardiography measurement, a new type of stable laser source control system is proposed to ensure that the laser can output at a constant power and a stable frequency. A precise temperature control system is designed to maintain a reliable temperature range. The effectiveness of the system is verified by experiments. The results show that the power drift value and wavelength drift value are 1.2uW and 0.9pm, respectively, which meet the requirements of system stability and accuracy.
FedGait: A Benchmark for Federated Gait Recognition

Gait recognition has been greatly improved by deep learning and can achieve a relative high accuracy. The advances depend on the data size of gait. However, due to public concerns on privacy and regulations and laws from different countries, it is very difficult and almost impossible to collect a huge centralized gait database for algorithm training. Federated learning is a distributed machine learning technique for privacy-preserving, and can help to solve the problem. We propose a federated gait recognition benchmark, FedGait, to train algorithms using distributed gait data. It is the first benchmark on gait recognition to the best of our knowledge. FedGait can utilizes the gait videos available on multiple clients to learn a robust and generalized model. Based on the real-world gait scenarios, we introduce two federated gait recognition scenarios: institution-based scenario (IBS) and device-based scenario (DBS). Compared with centralized training, federated learning will encounter more serious heterogeneous data and data imbalance problems. We employ four popular databases for experiments, CASIA-B, CASIA-E, ReSGait and OU-MVLP, are involved in FedGait to investigate the problems in federated learning. We hope FedGait is a good start to solve data privacy problem in gait recognition.