Miracle-dz/UniFAR-FTUs huggingface dataset:
Alternative Control Methodology of Grid-Supporting Grid-Forming Power Converter with a Proportional Complex-Vector Controller

An alternative control methodology for a grid-supporting grid-forming (GSGFm) power converter operating within a synchronous rotating reference frame (SRRF) is developed in this research. The proposed methodology combines a proportional (P)-based controller with a complex-vector (CV) controller to effectively mitigate distortions. The cascade voltage and current control structure employed in this methodology leverages a two-proportional controller to precisely regulate the dq-components of voltage and currents. Additionally, a two-complex-vector controller is utilized to attenuate periodic disturbances that may arise during operation. Experimental results have been conducted to evaluate and validate the performance of the suggested methodology. The obtained results provide empirical evidence supporting the effectiveness and robustness of the proposed control approach.
Indoor scene classification of robot vision based on cloud computing

For intelligent service robots, indoor scene classification is an important issue. To overcome the weak real-time performance of conventional algorithms, a new method based on Cloud computing is proposed for global image features in indoor scene classification. With MapReduce method, global PHOG feature of indoor scene image is extracted in parallel. And, feature eigenvector is used to train the decision classifier through SVM concurrently. Then, the indoor scene is validly classified by decision classifier. To verify the algorithm performance, we carried out an experiment with 350 typical indoor scene images from MIT LabelMe image library. Experimental results show that the proposed algorithm can attain better real-time performance. Generally, it is 1.4∼2.1 times faster than traditional classification methods which rely on single computation, while keeping stable classification correct rate as 70%.
Analyzing Derived Network Feature Importance to Identify Location Influence in LBSN

Location-based social networks (LBSN) data is of greater importance in various domains of studies, such as viral marketing, location recommendation, influence maximization problem, etc. Identifying influential or hotspot locations of disease spread is of great importance during epidemic outbreaks, in case of limited vaccination, to make timely decisions about stay-at-home policy, restricting the transportation/human movement from one geographical location to another, etc. LBSN data can be used to construct the location-based weighted spatio-temporal network with its defined feature sets, including its rich collection of user movement information with respect to space and time. Here, we design two algorithms to generate spatio-temporal edge weights in such networks. We then examine four benchmark algorithms to identify the importance of these derived edge features to evaluate location influence in the context of contagious disease spread. It is observed that deriving edge features play an important role in the application scenario, especially to understand the need of dense network compared to the sparse network, generated from the LBSN data.
Effects Analysis of the Integration of Distributed Generation on the Timed Overcurrent Protection: Necessity of New Settings on the Relay to Guarantee the Coordination Time

The growth of distribution systems with the inclusion of distributed generators (DG) is a reality that is taking place in several countries. The use of DGs in these systems brings benefits and challenges. One of these challenges is the effect of DGs on overcurrent protection, which makes it important to investigate through computational tools that evidence such effects and serve as a support for evaluating the necessary adaptations by the protection engineer. So, this work is a complementing of an earlier conference paper [1] that contains simulation analyses to evaluate these questions. For this, overcurrent protection devices were incorporated into the original system, in the places where DGs were inserted, for an evaluation of the network's behavior. The impact of these DGs was evaluated in the application of symmetrical three-phase faults in specific locations of the system, for a given DG size. An analysis of current variation, selectivity and protection coordination was performed in numerical and graphical form, in different scenarios. From the results obtained, it was observed that new settings for the protection devices are necessary so that the system remains correctly coordinated and protected with the inclusion of DGs.
The Heart Of The Antarctic Being The Story Of The British Antarctic Expedition 1907 1909

Thank you for reading the heart of the antarctic being the story of the british antarctic expedition 1907 1909. Maybe you have knowledge that, people have search hundreds times for their favorite novels like this the heart of the antarctic being the story of the british antarctic expedition 1907 1909, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some malicious bugs inside their computer.
Data Transmission in Automated Mining Quarry Management

Modern broadband communication systems equipped with high-precision location determination systems are actively used for the efficient data transmission in mining quarries management. This article presents an analysis of a specific example of using automated systems to manage mining quarries, where various wireless data transmission technologies are employed. The experience gained from implementing wireless technologies for real-time control of earth-moving and construction machinery in mining quarries can be valuable in the implementation of similar projects for vehicle management in other industries.
Fundamental Limits for Dynamic Path Parameter Estimation in Asynchronous ISAC Systems

Passive sensing based on the dynamic path signal is a key technology in integrated sensing and communications (ISAC), where clock asynchronism between transceivers poses a crucial challenge. While various passive sensing applications have been realized by analyzing the dynamic channel path signal reflected from the target, how accurately the dynamic path signal may be recovered from the noisy asynchronous sensing signal is yet known. This work investigates the error bounds for estimating the dynamic path angle-of-arrival and complex gain sequence (CGS). We provide mathematical analyses and numerical simulations to characterize the dependence of the error bounds on system configurations and multipath conditions. Our results demonstrate the increase of the error bounds due to the asynchronism and reveal the blind zone in dynamic path CGS estimation.
Multi-domain Integrative Structure-enhanced and Sparse-coding Network for Sparse View CT Reconstruction

In order to decrease X-ray radiation dose and imaging time, it is crucial to employ a significant technique of minimizing the number of projections. Nevertheless, the presence of down sampling projection data may result in a decline in CT image quality caused by prominent streak artifacts. In this study, we present a novel network called MISS-net (multi-domain integrative structure-enhanced and sparse-coding network) to enhance image quality using sparse-view data. To begin with, MISS-net integrates hybrid domain features from sinogram data and CT images through a two-step network structure. Furthermore, in each stage, a dedicated subnetwork for enhancing tiny tissue is utilized to ensure the preservation of texture, while a subnetwork for extracting features through learned convolutional sparse coding is employed. Next, a distinct domain transformation converts the projection into an image, facilitating the exchange of information between the two domains. Experimental results shown that our proposed MISS-net outperformed the comparison methods in terms of image quality, exhibiting enhanced small features and sharp tissue. Additionally, it exhibited minimal structural loss in image quality, as evidenced by the experimental results on 64 views MAYO clinical datasets.
High-Frequency Load Independent Electroporator Circuit Design for Nanosecond Pulse Generation

The technique of pores formation in the cell/tissue membrane by an applied electric field across the membrane is known as electroporation. The electroporation results in drug delivery through the cells but it suffers the challenges of inefficient transportation of drugs owing to the non-linearities of multicellular structure. However, medical applications require a pre-set of electrical pulses to be delivered to the load in order for electroporation to be effective. This paper presents a novel load independent electroporator design for nanosecond pulse generation. The presented electroporator is capable of forming rectangular pulses for a broad range of load parameters 10k to 100k. To generate short nanosecond pulses in the range of 5 ns, and 10 ns independent of the electroporator load, a variable load resistor ranging from 10k to 100k is used that overcomes the use of a crowbar driving circuit. The electroporator is capable of generating pulses with a pulse repetition frequency of 30 MHz and it is designed to handle bipolar and unipolar pulse sequences.
Ground Penetrating Radar Signal Processing Method Research for Hidden Cable Accurate Detection

Ground penetrating radar (GPR) has a broad application prospect in the field of trenchless detection of power hidden cables. Aiming at the problem of cluttered received signal and weak target waveform, robust principal component analysis (RPCA) is used in signal preprocessing to suppress clutter and highlight the target signal. To intuitively and accurately reconstruct cable distribution, an enhanced back projection imaging algorithm (BP) based on weighting processing and coherence factor (CF) correlation is proposed. The introduction of CF can calculate the coherence of the superposition process, so the artifact interference in the imaging results can be eliminated. Because the data processing of GPR has a high demand for real-time performance and accuracy, the clutter suppression algorithm and the enhanced imaging algorithm are deployed on the end-side radar host and cloud platform respectively, which forms the end-cloud collaborative echo signal processing flow for complex detection scene. Experimental results of emulated data and measured data all prove that, the proposed algorithm can effectively suppress clutter and has superior focusing effect in imaging. The influence of imaging artifacts can be significantly weakened, and the cable outline is clearly reconstructed, which has an engineering application value for the trenchless accurate detection of hidden cables.
83.17% Power Conversion Efficiency, 13.5 dB Power Dynamic Range Rectifier for RF Energy Harvesting Applications in 22nm FDSOI Technology

This study proposes a novel RF rectifier that has a high power conversion efficiency and wide power dynamic range suitable for RF energy harvesting applications. The proposed RF rectifier utilizes ultra low power (ULP) diodes replacing the diode-connected MOSFETs in a self-biased architecture of a cross coupled differential drive topology. The ULP diode resolves the limitations of higher leakage currents on diode-connected MOSFETs while maintaining the same performance when forward-biased. The design is implemented in 22nm FDSOI Technology. The novel RF rectifier presented a PCE of 77.56% at -20 dBm at 900 MHz with a peak PCE of 83.17% at -23 dBm, a dynamic range of 13.5 dB with a sensitivity of -16.5 dBm at $100 \mathrm{k}\Omega$ load.
Electric Vehicles Modeling: A Review

The increasing concern about climate change has been leading researches in many areas to investigate solutions for this issue. An emerging subject related to it is electric vehicles (EV). Although it is an important topic, there is still a lack of review papers about EVs modeling. In this context, this paper aims to bring a review about EV modeling to fulfill this gap. A wide bibliography review is made to cover different kinds of EV s modeling, in many aspects. As a conclusion, in some areas, authors do not diverge from each other, while in others may exist a large range of different ways to model the EV s. In this case, researchers should choose the modeling that fits the best for their research, which may vary in simplicity and computational effort.
Optimal Scheduling of Electric Harbour Craft Fleet Operations

Transforming the transportation sector plays an important role in achieving zero emissions in the race against climate change, in which maritime decarbonization draws attention to the promising area of harbour craft electrification. Compared to road electrification, harbour craft electrification is relatively new and brings new challenges due to its unique characteristics. One of the challenges is integrating refueling technology and fleet management with constraints from vessels, infrastructure, and operational profiles. In this context, the paper explores the operation philosophy and proposes a Mixed Integer Linear Programming (MILP) model for co-optimizing harbour craft fleet management and refueling technology using fast and slow charging at different times of the day. Simulation results with data from a real electric harbour craft stress the importance of operational profiles and provide insights for decision-makers in specifying charging infrastructures and fleets.
RLN: A Residual Lightweight Neural Network for Image Edge Detection

Aiming at improving the accuracy of recognition and decreasing the consumption of memory in the field of image edge detection, this paper proposes a streamlined and effectively residual light-weight edge detection neural network (RLN). Firstly, we introduce the residual structure and reduce the module scale to extract the image edge features, effectively preserving the edge features and obtaining a comprehensive and rich edge map. Secondly, we expand the range of receptive fields by using multiple sets of convolution to extract features, which reduces redundant information and further reduces the amount of model calculation. Finally, we use an Up-sampling module to equalize the label map. Experiments show that compared with the state-of-the-art lightweight edge detection neural network LDC, the ODS value on the BIPED dataset reaches 0.891 with an increase of 0.2% and the number of parameters is only 500KB with a decrease of 25.8%. The experimental results on the MDBD and BRIND datasets show that, compared with other algorithms, RLN can still achieve competitive results while reducing computational cost.
Lightning-Induced Overvoltage Peaks Considering Soil Parameters Frequency-Dependence and Multi-Layer Soils

Lightning-induced overvoltages are one of the main causes of distribution lines shutdown. Additionally, it is known that the soil resistivity highly impacts these induced voltages. Thus, this paper considers a recursive calculation to obtain an equivalent resistivity of any multi-layer structure considering the frequency-dependent electrical parameters of the soil in an approximate formula previously developed for the peak value of lightning-induced overvoltage in overhead lines. In addition, the suggested approach is more efficient regarding computation burden than conventional techniques since it estimates the overvoltage using equations instead of computing the lightning electromagnetic fields. This makes it a useful engineering tool that can be applied in practical situations. According to the results, it is possible to note the importance of using more than one layer of soil models that take into account the variation of soil parameters with frequency since overvoltage differences of up to 10 % when compared to independent soil parameters with frequency and equivalent resistivity of less than $1000\ \Omega. \mathrm{m}$.
"Distance-Centric Modularity: Unveiling Network Structures Through a Novel Graph Measure"

Networks, which are ubiquitous in the real world, can be used to model a wide variety of systems like computer, biological social and technical networks. Finding communities or clusters of nodes that are more closely connected to one another than to nodes outside the group presents a significant problem in network analysis . Identifying and characterizing the community structure which is significant in the study of networked systems is implemented in an effective way with the introduction of the measure "modularity". Here the discussions go through some of the commonly used modularity measures that have been proposed so far in the literature. Many of these measures are better suited for certain types of networks than others.
Statistical Evaluation of Classification Models for PCA Detection

A successful strategy to eavesdrop on the information exchange of legitimate users is by implementing a pilot contamination attack (PCA) during the process of channel estimation. Detection of such an intrusion can be accomplished at the BS and two promising methods that can be used for this purpose are 2-N-PSK and Shifted 2-N-PSK. As binary classification models, in this paper, the performance of both methods can be best evaluated through several commonly used statistical metrics, including statistical accuracy, precision and recall, specificity, fall-out and F1-score. Other useful information can be obtained from the Receiver Operating Characteristic (ROC) curve and the ROC Area Under Curve (AUC) that represent the trade-off between fall-out and recall. For the purpose of comparing the performance of 2-N-PSK and Shifted 2-N-PSK, in this paper each of the aforementioned metrics is extensively studied, and simulation results are provided and analyzed.
A Comprehensive Review on Early Diagnosis of Alzheimer’s Disease Detection

A progressive neurodegenerative Alzheimer’s Disease (AD) shrinks (atrophy) the brain and kills brain cells. On a global scale, Finland and the United Kingdom have the highest prevalence of AD, with 54.7 and 42.7 cases per 100,000 inhabitants respectively. In India, there are 14.60 AD cases for every 100,000 people and it is ranked 137th in the world and 13th in India among the 50 causes of death. At the beginning of the disease, a person may forget things, or have trouble remembering recent conversations. As the disease progresses, a person may experience severe memory loss and difficulty performing everyday tasks. Consequently, the only way to predict AD at an early stage is to use treatments that prevent the disease from progressing. This paper provides an overview of the current approaches to AD diagnosis and detection, with a focus on the use of Biomarkers and discussed the advantages and disadvantages of Machine Learning (ML) / Deep Learning (DL) techniques in early detection of AD. It also reviews the collection of AD datasets and the pre-processing techniques used in the studies. Although DL methods gives the best outcome measures such as accuracy, precision, F-measures, recall and Area Under the Curve (AUC) still DL techniques have some limitations that can be overcome via Quantum Computing (QC). QC is currently emerging new technology based on quantum theory where qubits are used to store the data instead of bits. It accomplishes 1000 times faster and also solves the complex problem within a fraction of time than the classical computers.
Fish tracking based on YOLO and ByteTrack

Computer vision has been increasingly applied in the study of fish behavior. However, accurate and robust detection and tracking of fish schools remains a challenging problem due to appearance changes and occlusion during fish locomotion. To address these issues, we first propose a fish detection algorithm based on Yolov5, which utilizes automatic annotation to construct a detection model for fish head regions. Then, inspired by the ByteTrack tracking algorithm, we associate high and low-scoring bounding boxes separately to resolve tracking errors caused by partial occlusion. Finally, we use the position and direction to build a cost function to tackle trajectory fragmentation caused by long-term occlusion. Experimental results on zebrafish videos demonstrate that our proposed method achieves better performance in terms of detection and tracking compared to existing methods.
On the use of parametric stator models for electrical machine vibration computation

Parametric stator structural models are used in the electrical machine optimization process and in system-level simulations for noise and vibration prediction. In this paper different models that differ in computational resources and prediction accuracy are explored. First, the analytical method used for stator eigen-frequencies computation is statistically compared with the finite-element method. Afterwards, the vibration displacement computed via the semi-analytical method, that uses an analytical formulation for the forced response, including modulation effects and tangential force factors, and eigen-frequencies coming from finite element analysis, is statistically compared with the results produced by the vibration-synthesis method, representing a pure finite-element method. Here, the air-gap forces excitation used in the forced response problem are computed using a finite-element electromagnetic parametric model. The trade-offs between computation time and model accuracy are discussed.
An Adaptive Quadrant Filter-based PLL Approach for Grid Connected Multifunctional Inverter

The present study developed a grid-linked versatile inverter approach based on an adaptive quadrant phase-locked circuit (PLL). The strategy suggested addresses irregular load patterns as well as non-ideal system values such as imbalance, harmonics, intensity change, and so on, which enhances the reliability of the power. An Intelligent Quad Filter-based Synchro Referring Frames (SRF) PLL will appropriately cope with electric supply amplitude and minimize the time separation of PLL outputs. This method detects amplitude and period while also extracting affirmative cycle elements for a system with one phase. Once tested for wavelength and amplitude aberrations, a typical SRF PLL will display the resultant aberrations. The developed PLL will lessen the trade-off between phase angle surveillance precision and responsiveness under skewed situations. This strategy serves as a regulatory mechanism for a single-stage single-phase system-connected versatile switcher for PV installations to optimize electrical performance at the PCC primary while simultaneously servicing the infrastructure and demand. When determining benchmark photovoltaic (PV) signaling as well as authority, the Perturb & Observe Maximum Power Point Tracking method is used. The article includes an in-depth investigation using the offered monitoring mechanism. MATLAB / Simulink is used to test the functionality and efficacy of its given regulation during diverse functioning situations.