Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Simulation Design of Mobile Communication Attenuation Model Based on Ant Colony Algorithm

The main purpose of mobile communication attenuation model is to optimize the network equipment of operators and the types of actual services through theoretical simulation calculation. In this paper, the attenuation model of mobile communication is studied based on ACA (Ant Colony Algorithm). Ant travel only takes place in one direction and may be delayed in congested nodes. The role of an ant when it reaches a certain node will decrease with the increase of the age of the ant. According to the characteristics of different test environments, different simulation methods are given, and the influence of some important factors on signal transmission is emphatically simulated and analyzed. The simulation results of path loss in outdoor to indoor walking environment show that the path loss increases rapidly from 80db to 125db in the first 900mm. Moreover, the outdoor path loss is slightly larger than that indoors, because there are abundant outdoor shelters, especially those obstacles such as trees and vehicles on the path will have a certain impact on the logarithmic normal shadow fading. It can be seen that this method improves the accuracy of the predicted attenuation value and provides a more accurate prediction model for the optimization of mobile communication air interface network.
Designing the Control Loop for Latching Current Limiters (LCL)

DC power systems find extensive applications in the automotive and aerospace industries, where there is a growing demand for higher voltage and current levels. Additionally, the utilization of DC power systems in microgrids is growing continuously. This increased adoption enhances the need for more efficient protective mechanisms to ensure reliable and secure operation. The latching current limiter (LCL) plays a crucial role in DC systems by serving as a protective barrier against overload. It functions as a safeguard, preventing excessive current from causing harm to sensitive circuitry and components. Implementing an LCL offers essential protection for these components, leading to extended lifespans and enhanced overall system reliability. A significant challenge in LCL development concerns the designing of the control loop. Traditional methods often rely on linearization and approximations, which can yield poor results. An alternative approach for control loop design involves practical methods, using the real circuits. This work introduces some experimental approaches to design the LCL control loop. The signal injection technique is used, in pulsed and continuous mode. The K-factor method is employed to define the compensator. The results are presented and compared, demonstrating the effectiveness of the proposed solution.
A Novel Machine Learning Algorithm for State of Health Prediction of Lithium-Ion Batteries

The development of Battery Management Systems (BMSs) able to determine the State of Charge (SoC) and the State of Health (SoH) of lithium-ion accumulators through a simple and cost-effective procedure is becoming of paramount importance considering their potential impact on two key enabling technologies of the 21st century, i.e., electrical mobility and energy production from renewable sources. In this paper, a novel machine-learning model, based on the Random Forest (RF) and suitable to BMS, is presented. The model is able to estimate SoC and SoH under different operating conditions by exploiting only impedance measurements derived from Electrochemical Impedance Spectroscopy (EIS). In particular, we provide an assessment of the proposed model by investigating its performance in terms of accuracy and reliability.
A Multi-Objective Optimization Schedule for the charging behavior of the EV user in a Smart Campus

Recently, electric vehicles (EVs) demand has been intensely increased. Accordingly, more electrical vehicle charging stations (EVCSs) are essentially needed. The charging behavior of the EV user is affected by several issues. This paper proposes a multi-objective genetic algorithm (MOGA) optimization scheme for scheduling the charging behavior of the EV user in a smart campus. The problem is optimally formulated and designed to minimize the total traveling distance of the EV toward charging station (CS), needed fully charging time of the EV battery, total charging cost, total expected queuing time in the CS, and needed energy consumption of the EV to arrive the CS. The considered system involves 50 EVs and 3 EVCSs. Few solar cells are employed to recharge the EVs and reduce the energy loss and voltage drop. Different scenarios are provided to compare between the proposed and uncontrolled (unoptimized) schemes. The results prove the proposed technique effectiveness. The EV user's charging behavior is improved. Moreover, it is concluded that the charging cost depends on the state of charge (SoC) of the EV batteries and charging time of the EVs.
An Optimized Binary Scheduling Controller for Microgrid Energy Management Considering Real Load Conditions

A dynamical power demand and stochastic nature of energy resources posses difficulties in controlling and managing output power. These challenges lead to instability and inconsistency of the entire operation which can cause unstable and power quality issues. This study presents an optimal schedule controller for microgrid energy management, utilizing the Binary Particle Swarm algorithm (BPSO) to minimize costs and ensure optimal power delivery to loads. The controller's aims include minimizing total operating costs for distributed energy resources and solving intricate constraint optimization issues with scheduling management operations. The proposed approach's effectiveness is evaluated within an IEEE 14-bus configuration with five microgrids (MGs) integrated with RESs using real load data from Perlis, Malaysia. The BPSO optimization technique offers an exceptional binary fitness function to find the optimal cell, utilizing real data such as solar radiation, wind speed, battery charging/discharging, fuel conditions, and demand. To confirm the efficiency of the developed controller, a comparison is conducted between the results achieved with and without microgrid (MG) integration. The results reveal the robustness of the BPSO algorithm in reducing energy consumption and cost by 199.6 MW to 316.53 MW and RM 87,250.35 to RM 138,327.5 respectively. As a result, an optimized scheduling controller-based BPSO optimization outperforms in terms of savings cost, reduced energy consumption, optimal DER use, and decreased CO2 emissions.
Long-term palaeomagnetic secular variation and excursions from the western Equatorial Pacific Ocean (MIS2-4)

New palaeomagnetic results are presented for the Pleistocene (MIS2-4) portion of deep-sea core MD98-2181 (MD81; Devao Gulf, Philippine Islands). MD81 is the highest resolution ( ∼ 50 cm ky − 1 ) palaeomagnetic secular variation (PSV) record for ∼ 12–70 ka ever recovered from equatorial latitudes ( ± 15 ◦ ). Magnetic studies indicate that MD81 has a stable natural remanence with directional uncertainties (MAD angles) typically less than 3 ◦ . We have also recovered a relative palaeointensity estimate from these sediments based on normalization to isothermal remanence. We have correlated our relative palaeointensity record with high-resolution relative palaeointensity records from the North Atlantic Ocean. The MD81 ages are always within ± 500 yr of the North Atlantic records over the entire core. We also correlate our PSV record with another published PSV record from Indonesia (MD34). We are able to correlate 25 inclination features, 25 declination features and 24 relative palaeointensity features between MD81 and MD34. We identify three intervals of ‘anomalous’ directions in the cores (based on > 2 σ deviation from mean directions). One of these intervals contains true excursional directions and is dated to ∼ 40.5 ka. We associate this interval with the Laschamp Excursion. We also note two other intervals that have anomalous directions, but no true excursional directions. These intervals occur around ∼ 34.5 and ∼ 61.5 ka and we associate them with the Mono Lake Excursion ( ∼ 33.5–34.5 ka) in western USA and the Norwegian-Greenland Sea Excursion ( ∼ 61 ± 2 ka) in the North Atlantic Ocean. We view our ‘anomalous’ PSV in the three intervals to be truly anomalous even though most directions are not truly excursional. We think that it is time to reconsider the deﬁnition of what is ‘anomalous’ PSV or excursions. To do that we need good-quality PSV records from several regions that have reproducible records of normal PSV, excursional waveforms and relative palaeointensity. We cannot assess the difference between normal PSV and excursions without such complete PSV records. This study is one attempt to develop such a regional perspective.
Fixed Frequency Sliding Mode Control of Distributed Generation in Microgrids for the Balanced and Nonlinear Loads

This paper proposes a novel control system integrating sliding mode control (SMC) with a lock-in amplifier (LA) to enhance the stability and power sharing precision in microgrids (MGs) with distributed generation (DG) units. The control system effectively mitigates harmonic distortions and maintains voltage amplitude and frequency under dynamic load conditions. The LA assists in extracting desired signals amidst noise, facilitating harmonic compensation in the inverter's control input signals. The proposed control system, tested under various conditions, demonstrated significant improvements in power quality, demonstrating reduced total harmonic distortion (THD) in comparison to existing methods, aligning with IEEE Std 519 standards.
The Role of System Modeling on Artificial Intelligence: A Review of Emerging Trends

System modeling is essential to the growth and comprehension of artificial intelligence (AI). It offers a way to describe, examine, and improve sophisticated AI systems. The significance of system modeling in AI is explored in this paper, along with examples of its use in robotics, machine learning, and natural language processing. We also discuss the difficulties and possible directions in system modeling for AI, highlighting its potential to further improve the robustness and capabilities of AI systems.
Third Harmonic Circulating Current Injection Effects on Optimum Silicon Area of Delta-CHB STATCOM

This work aims to identify the impact of third harmonic circulating current injection (THCCI) in a Cascaded H-bridge converter in delta configuration (Delta-CHB) for Static Synchronous Compensator (STATCOM) application. The analyses consist of evaluating the impact of THCCI on the silicon area of semiconductor devices, based on a typical IGBT/Diode model to determine the nominal current of the devices for a given maximum junction temperature. A coefficient of determination higher than 89% was obtained for all modeled variables. The results showed an increase of 42.4% in the active area of the IGBT and 42.2% in the active area of the diode when the peak cluster current increased by 62%.
A New Method and Implementation of Metamaterial-Enhanced Underwater Magnetic Induction Communication System

Aiming at extending the transmission range of underwater magnetic induction communication system, a method to enhance the magnetic signal strength by installing a metamaterial slab before transmitter/receiver is proposed this paper. A three-dimensional underwater magnetic induction system was modeled and simulated by COMSOL to determine the size and the optimal negative permeability of the metamaterial. By HFSS simulation, we design an electromagnetic metamaterial slab, together with induction coil it implements the metamaterial-enhanced antenna unit. Experiment platform was then built by using SDR-USRPs and the plastic-sealed magnetic communication antenna units. The results show that the communication range increases by about 56.8% at carrier frequency of 3.9 MHz by adopting this method.
Line-Start Permanent-Magnet Synchronous Motor: Axial-Flux vs Radial-Flux Configuration

This paper presents the design and analysis of an axial-flux line-start permanent-magnet synchronous (ALSPMS) motor. The motor includes a hybrid rotor which consists of both induction cage and permanent magnets (PMs). The paper compares the performance of the presented motor against a radial-flux line-start permanent-magnet synchronous (RLSPMS) motor with a similar rating. The performance parameters of the studied motors are predicted under steady-state and transient operation in both 2-D and 3-D finite-element analysis (FEA). FEA simulation results show that the transient response of the axial-flux (AF) motor in 3-D FEA differs with that of its equivalent 2-D FEA. The studied 4-pole ALSPMS motor offers comparable efficiency and power density against the conventional RLSPMS motor.
Workflow Design for Implementation of In-silico Experiments with Big Biomedical Data Streams

The presented article deals with a workflow for processing and analyzing big biomedical data streams. The proposed workflow is designed using the software for biological and biomedical image processing Cell Profiler. The structure of the workflow consists of twelve stages. It includes the steps of segmentation and greyscale image masking to produce effective image analysis. The structure of the workflow and the performed experiments are presented graphically.
Exploring the Effects of Various Generative Adversarial Networks Techniques on Image Generation

Generative Adversarial Networks (GANs) have rapidly risen to prominence in the sphere of deep learning. This is especially true when it comes to image generation, where GANs have displayed impressive capabilities. Over time, as researchers have grappled with the challenges posed by the original GAN model, a plethora of GAN variants have been introduced. These are tailored to counteract training instability, mode collapse, and various other challenges intrinsic to the base GAN model. Despite the extensive variety of GAN techniques available, there is still a knowledge gap when it comes to understanding their relative performances on specific datasets. For many practitioners, choosing the right GAN model for a given dataset remains a trial-and-error endeavor. In an attempt to shed light on this matter, our research undertakes a deep dive into three of the most notable GAN techniques: the classic GAN, the one built upon Wasserstein Distance, often abbreviated as W-GAN, and its subsequent evolution, WGAN-GP.
A Game Agent Design Method in Simulation Training System

During the simulation training process, the two sides involved in the training used the simulation training system to carry out a force confrontation. The design of game agents is the core problem that needs to be studied. Firstly, the implementation of the game adversarial framework in the simulation training system is expounded. Then, the basic functional structure of intelligent forces is expounded. Then, a game agent design method based on large language model kernel is proposed, and the execution process of the design method is elaborated. Finally, the specific application is carried out in the simulation training system, which proves the effectiveness of the proposed method.
The Compounding Consequences of Climate Change and Wildfire for a High Elevation Wildflower (Saxifraga austromontana)

iv Acknowledgments . v List of Tables and Figures . viii Chapter 1: Project Summary.1 Chapter 2: Why Georeferencing Matters: Introducing a Practical Protocol to Prepare Species Occurrence Records for Spatial Analysis . 7 Abstract . 7 Introduction . 9 Methods . 12 Study system: Saxifraga austromontana . 12 Historical Herbarium Record Data . 13 New Georeferencing Methods . 14 Species Distribution Models . 15 Climate Variables . 16 Background Selection . 15 Climate Space Analysis . 17 MAXENT Model Settings . 17 Model Evaluation . 18 Results . 18 Climate Space Analysis . 18 Species Distribution Models . 19 Discussion . 20 Conclusion and Future Efforts . 24 Tables and Figures. 26 References . 39 Chapter 3: The Compounding Consequences of Climate Change and Wildfire for a High Elevation Wildflower . 44 Abstract . 44 Direct and Indirect Effects of Climate Change . 46 High Elevation Ecosystems and Climate Change . 48 Fire Effects on High Elevation Plants . 49 The Study System . 51 Experimental Overview . 52 Methods . 54 Species Distribution Models . 55 Fire Records . 57 Determining Field Sites . 58 Field Observations and Collections . 58 Computational Analyses . 60 Fire and Climate Predictive Spatial Analysis . 61 Results . 63 Species Distribution Models . 63 Charcoal Analysis . 64 Population Comparisons . 64
How Choice Fatigue Affects Consumer Decision Making in Online Shopping

Although online shopping now accounts for a significant portion of all consumer sales worldwide, current e-commerce websites are usually inundated with various product options that result in information overload for consumers. Consumers find online shopping to be time-consuming, when they need to compare product options causing choice fatigue. Choice fatigue refers to the deteriorating quality of decisions made by an individual after a long session of decision-making. Although cognitive science acknowledges that choice fatigue affects the quality of decision-making, it remains unclear how it affects consumers' decision-making in the online shopping environment. Investigating that topic, this paper discusses whether consumers rely on decision-making shortcuts amid choice fatigue and whether the provision of such shortcuts increases their satisfaction. Empirical experiments were conducted through an online shopping platform that contained several versions of T-shirt selection pages to investigate consumers' choice. The results indicate that consumers reply on decision-making shortcuts regarding early options amid choice fatigue but not on the bestseller options. Moreover, there is no preference for the bestseller options even though bestsellers alleviate the burden of making purchasing decisions.
Joint Route and Power Optimization for Multi-UAV-Enabled Colocated MIMO Radar System

Unmanned aerial vehicle (UAV) is an effective and promising equipment to offer more opportunities for MIMO radar system to approach and percept targets in complicated environments. In this paper, we study the joint optimization of UAV route and power for target tracking in a multi-UAV-enabled Colocated MIMO (C-MIMO) radar system. The goal of the optimization is to conserve system power while meeting the tracking accuracy demands. We first determine the normalized posterior Cramér-Rao Lower Bound (PCRLB) as the tracking performance metric. Then, a non-convex inter-variable coupling optimization problem is formulated. To cope with the problem, we decompose it into two sub-problems and present an alternate optimization algorithm. For the optimization of UAV route, a successive convex approximation (SCA)-based method is employed. For the optimization of radar transmit power, two solution methods are provided, namely, the numerical solution based on semidefinite programming (SDP) and the analytical solution obtained by the Lagrangian dual method. Simulation results indicate the effectiveness of the proposed algorithm.
Design of Data Distributed Service-Based Distributed Co-Simulation Platform of Power Systems

Recent rapid developments of distributed renewable energy resources result in difficulties in dynamic simulations of comprehensive power system models. To speed up the simulation process, this paper presents a distributed co-simulation framework for simulation of large-scale power system. First, distributed and decoupled model of a large-scale integrated model power system is obtained via decoupling the entire power system model into interconnected sub-systems. Then, the model interface between components such as generators/load and the power network is developed. Meanwhile, the data distribution service (DDS) is adopted to enable a data exchange among components and different subsystems. Finally, an DDS-based distributed co-simulation platform with synchronous simulations of sub-systems is proposed and implemented under Matlab/Simulink environment. The proposed platform has been implemented and verified via two case studies. Simulation results confirm the accuracy and the scalability of the proposed co-simulation platform.
Modular Decision Making Framework for Level 4 Applications in Automated Driving

Decision making for automated driving according to SAE level 4 (L4) faces a major challenge in the urban environment. Previous research has presented a variety of approaches, which, however, mostly focus on decision making for individual maneuvers or use cases. To address the complexity and requirements of the urban environment, it seems useful to present a way to flexibly combine approaches from the literature. Addressing this challenge, we present a framework for decision making that allows a modular embedding of different methods and thus scalability. The framework includes all necessary processing steps and submodules within a functional architecture according to SAE level 4, starting from the environment model up to the trajectory planning. The central component is formed by automatically generated interaction areas, which serve as the basis for the modular structure of the downstream processing levels. The universal applicability of the framework is demonstrated by real implementations in three representative $L4$ functionalities.
Constructing the Simple Timeseries Event Model

Knowledge Graphs (KGs) are able to model complex entity-relation systems and can be used to explain the predictions of a Machine Learning (ML) (ML) model. Different kinds of KGs exist, such as Temporal KGs or Event KGs (EKGs). The Simple Event Model (SEM) was introduced in 2011, in order to model events from different domains in EKGs. However, the SEM lacks the ability to model human inference of causal relations for time series data. For instance, the effect of a specific political event on a consumer goods market, could not be modeled in an EKG using the SEM. Therefore, we present the Simple Temporal Event Model (STEM), an advancement of the SEM, that is able to store human inference of temporal event causality. After describing the classes and type systems of our ontology, we justify our model decisions. Furthermore, we demonstrate the interoperability of our ontology by providing an example population using real-world industry data and multiple benchmark graph datasets. The STEM can be used to model events together with time series data and human inference of causality, enabling future KG-based explainability approaches, such as KG-based reasoning for explainable time series forecasting.
Quality Prediction of PECVD Process with Random Forest and Long Short-Term Memory Neural Network

Plasma enhanced chemical vapor deposition (PECVD) is a key process for producing the surface thin film of high-energy batteries for that it could enhance the absorption and energy conversion rates of solar cell films. While the PECVD coating process is complex enough to be realized and requires various sensors to collect data as well as monitor the quality throughout the process. This paper first performs correlation analysis on the collected data, examines the relationship between the multi-sensor data, and proposes a hybrid algorithm of random forest and long short-term memory network (LSTM) to predict the relationship between the process parameters and the quality parameters data collected by multiple sensors in PECVD process. In this hybrid algorithm model, the importance of multi-sensor data is extracted by random forest module, and the time series features of the data are analyzed by LSTM. The proposed novel method overcomes the limitations of the existing algorithm models that only consider either temporal features or important features, which are not comprehensive enough for practical applications. The result reveals that the RF-LSTM improves the prediction accuracy by 40% without significantly increasing the iteration time.