Miracle-dz/UniFAR-FTUs huggingface dataset:
Load Demand Forecasting in Smart Microgrid Using NWP via Feature Engineering

This study developed a machine learning model to forecast electrical demand for smart micro-grids with the existing of numerical weather forecasting (NWP). The model uses three techniques, linear regression (LR), Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost), to harness insights from historical data and predict future load patterns. Notably, the XGBoost algorithm is highlighted as the most effective due to underscoring the importance of feature selection and model optimization. The utilization of these techniques results in accurate electrical demand predictions, which are crucial for enhancing micro-grid efficiency, practically in response to weather-induced load fluctuations. This paper suggests employing advanced feature selection methods based on high feature importance, ultimately refining the model’s predictive capabilities for smart micro-grid applications.
Construction and Application of Power Grid Fault Handling Knowledge Graph

The fault handling emergency plan of power grid has great instructive significance for the quick and proper emergency disposal when the failures or accidents occur. To solve the problem of poor application effectiveness of the current fault handling emergency plan, a new method to construct the knowledge graph for the fault handling based on the BERT-BiLSTM-CRF model is proposed. In response to the problem of insufficient data available for deep learning model training and high data labeling costs in the power grid, the knowledge extraction module utilizes Bert pre-training method to construct the entity recognition model to improve the performance. The experimental results show that F1-score reaches 86.75% and the accuracy rate reaches 95.81%. Finally, the Neo4j graph database is adopted for highly visual management of the knowledge graph. When power grid faults occur, the fault handling knowledge graph can assist dispatchers to improve the capabilities of power grid's emergency disposal and the level of dispatch intelligence.
A Hybrid Active Flux Observer Based Encoderless Control Method for AC Motor Drives

This paper addresses a novel hybrid active flux observer based encoderless control scheme for AC motors. An Extended State Observer (ESO) is used to combine voltage model (VM) and current model (CM), and stabilizes the critically stable VM. Moreover, an adaptive law is designed to identify and compensate the voltage errors caused by inverter nonlinearities. The low-speed operation performance is improved via the proposed control scheme. Experimental results conducted on an asynchronous motor verify the effectiveness of the proposal.
Objective Assessment of the Bias Introduced by Baseline Signals in XAI Attribution Methods

This work represents a first step towards a systematic analysis of the impact of the choice of the baseline signals to be used in explainable baseline-dependent methods for multi-modal and multi-dimensional data relying on single-input deep networks, in view of the generalization to multi -channel architectures. This point is critical for ensuring the soundness of the attribution values and enabling their subsequent validation through association studies. In this work, two different CNNs were implemented to predict Alzheimer's disease patients from control subjects using structural Magnetic Resonance Imaging volumes and genetics data. The Integrated Gradients method was applied to both models for post-hoc attribution visualization relying on different baselines. Differences in the attribution maps were found with respect to the attributions of the reference baseline in both modalities highlighting the importance of finding and using the “optimal” baseline. We believe this work is highly relevant for the community in the framework of the validation of XAI post-hoc methods, as it provides evidence of the impact of the choice of the baselines for deriving feature attribution values with the Integrated Gradients method which determines the reliability of the outcomes, improving both the awareness of the users and their trust in the methods.
A 10-Year Austral Summer Climatology of Observed and Modeled Intraseasonal, Mesoscale, and Diurnal Variations over the Maritime Continent

AbstractThe Maritime Continent is one of the wettest regions on the planet and has been shown to be important for global budgets of heat and moisture. Convection in the region, however, varies on several interrelated scales, making it difficult to quantify the precipitation climate and understand the key processes. For example, the diurnal cycle in precipitation over the land varies substantially according to the phase of the Madden–Julian oscillation (MJO), and the diurnal precipitation cycle over the water is coupled to that over the land, in some cases for distances of over 1000 km from the coast.Here, a 10-yr austral summer climatology of diurnal and MJO-scale variations in rain rate over the land and sea over the Maritime Continent is presented. The climatology is based on mesoscale model simulations with a horizontal grid length of 4 km and satellite precipitation estimates. The amplitude of the observed diurnal precipitation cycle is shown to reach a maximum just prior to the MJO active phase, with.
Design and Control of a Cable-Driven Upper Limb Exoskeleton Robot for Rehabilitation

Stroke is a significant disease that poses a serious threat to health. Rehabilitation robots can assist stroke patients in completing rehabilitative movements, promoting their recovery while alleviating the workload of healthcare professionals. We design a cable-driven upper limb rehabilitation exoskeleton that separates the driving mechanism from the execution mechanism. This innovation significantly reduces the exoskeleton's weight and improves the wearing experience. We then compute the kinematics of the cable-driven system and develop a posture-based position control strategy. We construct prototypes of the exoskeleton's mechanical structure and control system, followed by conducting wearing and movement experiments. The results of the experiments indicate that the exoskeleton provides a favorable wearing experience and achieves a posture control accuracy of within 4 degrees.
Comparative Performance of Direct and Energy-based Controllers to Compensate Internal Currents of Modular Multilevel Converters

This paper presents a comparative study between direct and indirect, or energy-based, current controllers to compensate circulating currents of modular multilevel converters. Despite being easy to implement, direct suppression controllers can produce oscillations on the dc side current of the MMC during transients. On the other hand, energy-based controller allows complete control of the internal dynamics of the converter, attenuating the dc-current oscillations during transient. Digital simulation results obtained with a modular multilevel converter with 20 submodules per converter's arm, modeled in the PSCAD/EMTDC program, will be used to demonstrate the performance of each compensation technique.
FCS-MPC for Five Level Common Ground Single-Phase Multilevel Converter for Photovoltaic Application

The integration and control of renewable energy systems is a critical issue, especially in applications with a wide range of voltage levels, multiple sources, and different connections to the grid. This work presents the application of the Finite Control Set Model Predictive Control (FCS-MPC) in a common 5-level grounded topology for photovoltaic integration to the grid. In addition, the converter and load model required for FCS-MPC is presented, highlighting the simplicity of its implementation. The proposed control method achieves fast response with multi-objective control using a single objective function. Steady-state and dynamic simulation results at varying current reference are provided in this paper.
Methodology for agave plants recognition based on superpixels segmentation and drone images

The tequila and agave culture are important symbols of the national identity in Mexico. In the last decade, the production of tequila has increased and improved the care of Tequilana Webber Blue Agave. In this paper, we introduce a methodology based on the use of Unmanned Aerial Vehicles (UAV) as drones, image processing techniques and machine learning approaches for the detection of agave plants. Several landscape images of agave crops are collected from the Romita region in Guanajuato state. These images are processed to obtain orthomosaics which allow us to study all the target region of the agave crops. The orthomosaics are segmented using superpixels techniques in order to define features which allow us to recognise the agave plants. For experimental purposes we compare five segmentation methods in order to evaluate the performance for the detection of agave plants. Random Forest Classifier Algorithm (RFC) is implemented for classification purposes. Our proposal achieves 97% of accuracy to detect the agave plants.
Multi-granularity Dynamic Identity Aggregation for Text-based Person Re-identification

Text-based person re-identification(ReID) is a text-to-image retrieval task that searches images of people across non-overlapping cameras with textual descriptions. Due to significant intra-class variations and cross-modality disparities, along with substantial sample noise, it becomes challenging to learn discriminative features. Existing text-based ReID methods tend to focus on learning global representations, which possess limited discriminability and weak robustness to noisy samples. In this paper, we propose a novel Multi-granularity Dynamic Identity Aggregation(MuDIA) learning approach that simultaneously explores intra-modal fine-grained and cross-modal coarse-grained contextual cues for text-based ReID. MuDIA consists of two primary components: Weighted Parts Aggregation for Global Feature (WPAG) and Multi-Granularity Refinement of Local Feature (MGRL). WPAG aims to learn discriminative features by aggregating parts and simultaneously exploring the contextual relations among body parts within each modality. MGRL aligns the features of the two modalities at multi-granularity, eliminating the modality gap at cascade-style levels and gradually reducing the feature gap between the visual domain and the textual domain. Extensive experiments are carried out to evaluate the proposed framework on two text-based ReID datasets, i.e., CUHK-PEDES and ICFG-PEDES.
Analyzing Dynamic P-Q Capability and Operational Abnormalities of PMSG Wind Turbines

With the proliferation of large-scale grid-connected wind farms, subsynchronous oscillations (SSOs) incidents associated with Type-4 wind turbines (WTs) with a permanent magnet synchronous generator (PMSG) occurred frequently. These incidents have caused severe reliability risks to the power grid. Conventionally, P-Q capability charts are utilized to ensure the safety operating region of a synchronous generator. However, a PMSG WT exhibits a complete different and dynamic P-Q capability characteristics due to the difference in energy conversion technique and several other critical factors related to the power converters of the WT. This paper presents a comprehensive dynamic P-Q capability study of a PMSG WT with sufficient and accurate considerations of the WT control and operation in the dq reference frame, its specific power converter constraints, variable grid conditions, etc. Models of a PMSG WT are first developed based on its control principle in the dq reference frame. Then, algorithms for obtaining the P-Q capability charts of the WT are developed with the considerations of complete WT constraints in different aspects. The proposed study is verified via an electromagnetic transient (EMT) model of a grid-connected Type-4 WT.
Cross-Subject Mindfulness Meditation EEG Decoding

In recent years, assisting meditation using neuro-feedback applications has become increasingly popular. One key component of such applications is the ability to accurately decode the state of meditation from electroencephalography (EEG) signals in real-time, with as small calibration as possible. This work investigates the problem of cross-subject mindfulness meditation decoding from EEG signals. For this reason, a dataset containing EEG recordings from Novice and Expert meditators is employed. First, Riemannian Space Data Alignment (RSDA) is performed in a session-wise and subject-specific manner to tackle the problem of subject variability and within-session shifts. Then, after a comparative study among features used in the field of meditation, the performance of feature engineering methods is compared to a deep learning-based approach for decoding the EEG state of meditation. In this work, the EEGNet was employed for the deep learning approach, an architecture with a small number of learnable parameters widely used in the Brain-Computer Interface (BCI) field. The EEGNet applied to Riemannian space data aligned EEG signals leads to the highest decoding performance using the smallest time segments. The results show that EEGNet can effectively extract relevant features from EEG signals for decoding the state of meditation in small time segments, which has important implications for developing more effective and calibration-free neurofeedback applications for facilitating meditation.
Electrical Treeing Simulations to Understand the Applicability of Needle-Plane Tests to Cables

Electrical treeing in lab-based testing currently requires the use of field enhancement points such as needle tips or thin wires. While these techniques produce reliable electrical tree growth, it is important to understand the limitations of such tests in the context of the different geometries found in high voltage assets. Here we apply Finite Element Analysis tools to investigate the consequences of using needle-plane geometries and to develop ideas on how treeing within a more uniform field may relate or differ. This work finds that local geometrical effects present in non-conductive trees in more uniform systems, responsible for variation in field magnitudes are unlikely to be replicated in needle-plane tests.
Stability of Feature Detection Algorithms in Low Quality Video Encoding

Feature detection and description are the two enabling factors for image-based Augmented Reality, as they allow training on the images to be recognized for the superimposition of virtual objects. The aim of this paper is to evaluate the impact of codec and encoding parameters, which determine the quality of a video, on the stability and robustness of some of the most widely used methods for feature detection. By means of a frame-by-frame comparison between uncompressed YUV videos and the H.264/H.265 compressed versions, the stability of keypoints detected by ORB (Oriented FAST and Rotated Binary Robust Independent Elementary Features), SURF (Speeded-Up Robust Features), SIFT (Scale-Invariant Feature Transform) and BRISK (Binary Robust Invariant Scalable Keypoints) and the matches between them in the corresponding frames were evaluated. The results revealed the highest number of keypoint matches for SURF and ORB and the lowest number of matches for SIFT.
Temporal Gate-Attention Network for Meta-path based Explainable Recommendation

Heterogeneous Information Network (HIN) has garnered significant attention in the field of explainable recommendation based on paths due to its rich semantic information. However, many existing explainable recommendation systems solely focus on the connection information between nodes in a HIN while neglecting the temporal information between nodes. This leads to reduced accuracy and explainability of recommendation. For example, in a HIN of users and merchandises, many existing methods only consider the purchased merchandises by users, whereas our approach takes into account the temporal order of users' purchases, resulting in the creation of a new HIN. Additionally, we consider the possibility of unrelatedness between the previous and next merchandise in the sequence of users' purchases, which is a factor overlooked by current methods. Therefore, we propose the Temporal Gate-Attention Network for Meta-path based Explainable Recommendation(TGAMER). Our model utilizes gate recurrent unit(GRU) to capture the temporal information between items. Furthermore, it introduce a novel gate-attention mechanism to enhance feature aggregation among similar items while eliminating it among dissimilar ones. Extensive experiments conducted on three real-world datasets provide compelling evidence of the effectiveness of our model in terms of recommendation performance.
Evaluating the Interrelationships of Driving Factors of Industry 4.0 Maturity Models in Developing Countries Using Fuzzy DEMATEL

Many companies strive to incorporate Industry 4.0 into their strategies, considering it the upcoming major trend with new benefits. As it continues to evolve, industrial growth and competitive advantage must understand the key factors driving Industry 4.0's development. Despite the benefits of Industry 4.0, its impact on employment, wealth and distribution in developing countries is not fully understood. In emerging economies, companies often possess restricted capabilities and typically function at a considerable distance from the frontier. Furthermore, businesses, especially small and medium enterprises (SMEs), do not understand the tangible benefits of Industry 4.0. At the same time, the relationships of key driving factors could differ in the context of developing countries regarding limited infrastructure, workforce skills, or different regulations. This paper examines the interrelationship of Industry 4.0 maturity models driving factors. 16 driving factors were extracted from 30 references primary studies through a structured literature review and taxonomy development and grouped into 6 domains. By using expert opinions from developing countries, the study employs a linguistic fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method to quantify the interdependencies of each factor. This study found that although categorized as an affecting factor, strategy for I4.0 has the most relation to others. The findings presented in this study offer insights into the relative importance of I4.0MM driving factors, enabling policymakers, industry practitioners, and researchers to prioritize their efforts and allocate resources effectively as part of their adoption and growth strategies.
Closed-Loop System to Control Discharge Level for Studying the Effect of Long-Term Corona Discharge

In order to study the effect of long-term partial discharge on dielectric gases and determine the relation between gas compositions and PD levels, it is necessary to hold discharge magnitudes in a narrow range. This is not easy due to continuous change in the dielectric medium's chemical composition as well as potential erosion of high voltage conductor and insulator surfaces. A closed-loop control system is proposed here that adjusts the output voltage of the test transformer via a motor-operated variac. As a result of the verification tests under ambient, nitrogen, and a $C_{4}F_{7}N/CO_{2}$ mixture, the average partial discharge magnitude remained constant with an $\pm 3\%$ error.
Research on Static Reverse Analysis Technology for Security Detection of Power Industrial Control Software

In the power industrial control software, defects of software and possible pre-implanted backdoors and malicious codes are important factors that threaten the security of the power industrial control system. The complete and accurate extraction of function call relationship is the basis of software security detection based on function call graph. Therefore, based on analyzing function call sequence of abnormal behavior of electric power industrial control software, a method of software static reverse analysis is proposed. The method identified the function call relationship optimized by the compiler to be a jmp instruction by searching the call/jmp instructions. In order to extract a simplified key function call relationship, a key function list was obtained by performing keyword fuzzy matching. Experiments show that this method can accurately and comprehensively obtain the function call relation, the simplified key function call relationship can improve the efficiency of reverse analysis and provide a better support for software defects, backdoors and malicious codes detection.
Design of A Hybrid Compression Algorithm for High-fidelity Synchro-waveform Measurements

Real-time monitoring and analysis of distributed grids require synchro-waveform measurements, which capture almost all high-frequency disturbances and transient phenomena. However, due to limitations in high-speed measurements and network bandwidth, it is challenging to transfer all high-fidelity synchro-waveforms losslessly and successfully. To cope with this issue, a hybrid compression algorithm is proposed to significantly improve the compression efficiency for synchro-waveform measurements. Additionally, an error-index that combines the time and frequency domain error and correlation is developed to evaluate the waveform distortion. By integrating compression ratio, suitable parameters can be optimally selected. Finally, the simulation and laboratory experiments are conducted to verify the effectiveness of the proposed algorithm. The results demonstrate that a compression ratio of approximately 13 can be reached, corresponding to a 92.3% reduction in data space.
Monocular Camera Localization for Automated Driving

Localization in indoor applications is usually performed by means of environmental sensors such as lidar, mono and stereo cameras. Nowadays, cost-efficient monocular cameras are widely used in series production vehicles for driver assistance systems. In the context of higher automation, however, they face the problem of missing depth estimation, so that true-scale information cannot be extracted. As a result, localization maps generated with Visual SLAM exhibit arbitrary scaling in addition to the typical drift. Therefore, high-precision localization in a globally referenced coordinate system is not feasible. Addressing this challenge, we present an approach to achieve highly precise localization using only a monocular camera. Based on the opensource algorithm ORB-SLAM3 and ground truth data, we first generate globally referenced, highly accurate feature maps. In the second step, we evaluate the localization in real-world experiments using representative scenarios such as valet driving at the end-of-line at Ford Motor Company's manufacturing plant, automated valet parking in parking garages and outdoor driving in semi-structured areas. Emphasis is placed on investigating suitability under varying influences such as time of day, weather and environmental objects. In typical cases, we achieve an average accuracy of about 3 cm with maximum deviations of up to 8 cm.
Control of SCIG Based on Wind Energy Conversion System with BESS

The main contribution of this paper is to present a configuration for a wind energy conversion system using a Squirrel Cage Induction Generator (SCIG) with battery energy storage system (BESS). In the proposed system, the SCIG is connected to the power grid through a back-to-back converter, and a maximum power point tracking (MPPT) algorithm is employed to extract the maximum wind energy. The BESS is used to supply the power demanded by the grid even during variations in wind speed, and it is connected to the back-to-back converter via a DC-DC converter (buck-boost), which controls the battery bank and maintains a constant DC-link voltage. The system was modeled and simulated using Matlab/Simulink software.