Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate Bitcoin price prediction based on LSTM tuned by hybrid reptile search algorithm

This study focuses on analyzing historical data to forecast future trends in Bitcoin prices due to its influence on the business landscape. Its high volatility attracted attention to understanding the influencing factors for its price. This paper presents an empirical investigation using time-series data of various exogenous and endogenous variables. Closing prices of Bitcoin and Ethereum, along with the daily volume of Bitcoin-related tweets are examined for Bitcoin closing price prediction by a long-short term memory (LSTM) network, fine-tuned by a hybrid adaptive reptile search algorithm. The analysis covers a three-year period, in which data is divided into training, validation, and testing sets. Comparative analysis against LSTM networks tuned by other high-performing metaheuristic algorithms demonstrates that the novel approach outperforms competitors in terms of standard regression metrics.
Economic-Driven Distributed Secondary Optimal Frequency Control for Microgrids with Time-Varying Loads

This paper is concerned with optimal frequency control of microgrids with time-vary loads. A distributed strategy incorporating a dynamic average consensus algorithm is proposed for microgrids such that frequency regulation and economic dispatch can be achieved simultaneously. Based on the theoretical analysis, it is shown that the convergence of the proposed algorithm can be ensured. Finally, an illustrative example on the IEEE 14-busis presented to verify the effectiveness of this method,.
Performance in Programming Class and Basic Math Skills

Basic mathematics skills and knowledge are known to be effective predictors of students’ academic performance in university mathematics courses. No such indices, however, have been proposed for performance in programming courses. We analyze the relationship between basic math skills and students’ academic performance. In our test setting, students are given ten minutes to answer several questions on a sheet of paper before the first lecture in a programming class. We found very little correlation between scores on the basic math test and performance in programming. This result suggests that programming skills depend less on mathematical skills and likely much more on how seriously one engages in learning. Our future work includes a more descriptive and detailed analysis of the relationship between math skills and programming performance.
Model transferability for wave height prediction

Machine learning methods have been used to estimate missing data collected by wave buoys. The developed methods need a large amount of historical data related to wind conditions to estimate the missing wave heights. To deal with this problem, in this paper, the suitability of a deep neural network, composed of a cascade of convolutional and long short-term layers, to model transfer, is analyzed. Different methods are proposed and validated in a case study, showing the possibility of transferring knowledge acquired from a source buoy to a target buoy with a limited amount of data.
Branch Prediction in CPU Pipelining

Branch prediction is a prominent technology used in pipelined CPUs to help reduce the delay experienced in preventing control hazards. There are many different branch prediction techniques and algorithms proposed ranging from static to dynamic, each of which varies in terms of accuracy in predicting incoming branch directions. In this research paper, we use a simulation to test five different branch prediction algorithms and analyze the values obtained from their trace files, as well as the clock cycle times of each run. From the results, the static algorithms performed the worst and the dynamic algorithm based on a neural network performed the best. However, the ideal branch predictor cannot be determined solely by these results.
Physical Layer Security for UAV-Assisted IoT Data Collection in the Presence of an Aerial Eavesdropper

In this paper, we study physical layer security of an unmanned aerial vehicle (UAV)-assisted communication system, where a UAV collects data from a sensor node and UAV-eavesdropper tends to overhear confidential information. In particular, assuming Fisher-Snedecor fading environment, we derive the closed-form expression of the intercept probability and we examine the effect of different system/channel parameters on this performance metric. Numerical results are also presented to verify analytical result; and to highlight the impact of specific fading/shadowing conditions over the main-UAV link and UAV-eavesdropper’s link, as well as the impact of UAVs’ positions.
Stability and Performance Analysis in Two-Parameter Controllers for Weak Grid-Connected VSCs

In this study, we conduct an analysis of power control in a Voltage Source Converter (VSC) connected to a Weak Grid (WG), focusing on the system's stability and performance. Our main objective is to predict the behaviour of the VSC under different conditions. To address this analysis and enhance the control capabilities, we propose a two-parameter control structure and we develop a controller design methodology that ensures specific criteria are met, such as maintaining maximum voltage deviations at the Point of Common Coupling (PCC), achieving a minimum acceptable Short Circuit Ratio (SCR), and attaining the desired time response. Additionally, we establish criteria to avoid problems associated with grid synchronization using a Phase-Locked Loop (PLL). Finally, through representative simulations, we validate the accuracy of our predictions regarding the system's behaviour. Furthermore, the proposed design procedure proves to be highly effective in achieving the desired outcomes. This study contributes to the understanding of power control in VSCs connected to Weak Grids, providing valuable insights for designing robust and high-performing systems in such scenarios.
A Static Voltage Stability Margin Evaluation Approach for Coordinated Operation of Grid-Tied Wind Power, PV and Energy Storage Stations

Static voltage stability of power grids will become more sensitive to the coordinated operation of renewable energy resources (RESs) and energy storage systems (ESSes) due to their different output characteristics. This paper presents a generalized approach for static voltage stability evaluation under coordinated operations of wind power, PV and energy storage stations. First, a combined model for coordinated operation of stations which describes the output characteristics of active and reactive power is established. Then, the integration point of stations is optimized by modal analysis such that the most sensitive bus in the system can be located. Afterwards, the static voltage stability margin can be evaluated for load buses via impedance modulus margin index (IMMI). Furthermore, impact of penetration rates and penetration rate ratio among wind power, PV and energy storage stations on static voltage stability margin is investigated. To validate the effectiveness of proposed method, a standard IEEE 14 bus test system is studied and numerical results are presented.
-Forecasting Chinese Electric Vehicle Volume Using Keyword Search Data Based on SARIMA-LSTM Model

China has become the world's largest market for electric vehicles. This article proposes a hybrid forecasting method that combines SARIMA(Seasonal Autoregressive Integrated Moving Average) model with LSTM(Long Short-Term Memory) neural network to address the issue of electric vehicle volume. The SARIMA-LSTM model is combined with network keyword search index to improve its accuracy in predicting electric vehicle volume. The article first uses SARIMA model to make a preliminary linear prediction of the trend of electric vehicle volume, and then uses LSTM neural network to further optimize the SARIMA prediction results, improving the accuracy of the prediction. Through the comparison of SARIMA models with and without accelerated network search index, it is found that adding network search index can improve the prediction accuracy of SARIMA. Finally, the SARIMA, LSTM, and SARIMA-LSTM models are compared, and it is found that the SARIMA-LSTM model has the highest accuracy. Simulation experiments show that this method has the best prediction effect.
Distributed Event-Triggered Power Optimization of Multiple Smart Homes in Microgrids

This paper proposes distributed, event-triggered, power-sharing strategies to solve the economic power dispatch (EPD) problem for virtual energy storage systems (VESS) composed of smart homes in microgrids. Existing literature on this subject requires continuous or periodic communication. In this paper we propose a dynamic, event-triggered, information delivery strategy, combined with a weighted, distributed optimization algorithm. By inheriting economic parameters, the proposed algorithm guarantees power balance at each iteration, while power capacity constraints are guaranteed by deriving a virtual energy price for each smart home. These techniques enable the proposed resilient, distributed algorithm to solve EPD problem online with reduced communications. Theoretical analyses demonstrate the convergence and optimality of the proposed algorithm, which also excludes Zeno triggering behavior. Numerical analyses and simulations demonstrate the validity and effectiveness of the proposed strategies.
A Real-Time Hardware-Accelerator-Aided MJ-EKF SLAM Algorithm for Large-Scale Map Based on Heterogeneous Multi-Core SoC

As a classic SLAM algorithm, LIDAR-based EKF SLAM still remains a problem. An open issue is high computational complexity, which leading a very limited map size to sufficient real-time requirements. To address this problem, in this paper, we propose an MJ-EKF SLAM system that is fully implemented on a Heterogeneous Multi-Core SoC(HMS). To limit the EKF SLAM computation complexity, the Map Joining algorithm participates in the framework. The most computation cost step in EKF SLAM and Map Joining algorithms is implemented on an MJ-EKF dedicated accelerator. Meanwhile, several hardware optimization methods are proposed to save logic resources, avoid redundant computation and reduce data communication between on-chip memory and off-chip main memory. Field experiments demonstrate the HMS-based MJ-EKF algorithm achieved high real-time performance (over 30Hz) in a large-scale map with 500 landmarks.
High-Efficiency Quasi-Single-Stage Battery-Supercapacitor Hybrid Energy Storage System

The battery-supercapacitor hybrid energy storage system (HESS) integrates high-energy-density units and high-power-density units together, which has been widely used in microgrids (MGs) applications. However, the conventional HESS has the disadvantages of low efficiency and large volume. To improve the system power density, the quasi single-stage con-verter is an attractive solution for HESS due to it offering direct power flows from dc-side to ac-side. To maximize the system efficiency, this article proposes a novel space vector modulation with its basic idea and implementation process. Finally, the effectiveness of the proposed modulation is verified by quasi-single-stage converter-based virtual synchronous generator (VSG) experimental tests.
A Fast and Smooth Planning Framework for Autonomous Mobile Robot in Complex Environments

This technique note investigates the problem of path and motion planning for autonomous mobile robots in complex environments. A hybrid algorithm composed of a smoother A-star $(\mathrm{s}-\mathrm{A}^{*})$ algorithm for global path planning and a faster & more accurate Dynamic Window Approach (fma-DWA) is proposed. Firstly, three technical tricks are designed based on the standard $\mathrm{A}^{*}$ algorithm to ensure the smoothness and safety of the generated paths, which are the improvement of evaluation function, the redundant node deletion strategy and the B-splinebased path smoothing respectively. Then regarding the motion planning phase after path generation, we further advance the traditional DWA to choose a faster and more accurate motion command of the mobile robot. To this end, a composite evaluation function of the DWA is proposed by further considering the curvature of the generated path and the distance between the mobile robot and the target. Besides, the weight of the robot's velocity in the evaluation function is adjusted according to the closest distance to obstacles instead of keeping fixed in the traditional DWA method. Finally, extensive simulation results show the efficiency and accuracy of the proposed methods.
Research on Nodes Similarity in Complex Networks Based on Multistage Path Distribution Relative Entropy

In the complex network system, the similarity measure of nodes in the network is an important topic in the current research, which is of great significance for the study of complex network structure. In view of the current research situation, this paper proposes a method of relative entropy of multi-order path distribution to measure the similarity of nodes, and calculates the non-repeatable multi-order paths of edges and points in the network. This method can choose the order at will. Compared with other methods, it not only has less calculation, but also considers more multilateral information, making the similarity more sufficient. The comparison test of the real network data set shows that the method performs better in the mutual similarity and propagation influence index and the average aggregation coefficient evaluation index.
Snore Sound Recognition via an Explainable Capsule Network

Snoring can be caused by an upper airway reaction while sleeping. Classifying the excitation locations of snore sounds accurately provides assistance for treating snoring. In this research, we propose a convolutional neural network combined with a Capsule Network (CapsNet) to solve this problem. The models were trained and tested on the Munich-Passau Snore Sound Corpus (MPSSC), a relatively small and imbalanced dataset that contains four classes. As a result, the proposed method achieved an Unweighted Average Recall (UAR) of 58.5 %. Furthermore, we explained the working principle of the CapsNet through visualization, which could be helpful for understanding the generation of the results.
Supervised Learning for Non-Invasive Pre-Diabetes, Type 1 and Type 2 Diabetes Screening

In this research we present preliminary work using data extracted from the 2014 Behavioral Risk Factor Surveillance System data published by The Center for Disease Control and Prevention. Through the statistics provided by the Center for Disease Control and Prevention (CDC) on both type 1 and type 2 diabetes, it is noticeable there is a significant rise in diabetic cases. Diabetes is a persistent medical condition that disrupts the body’s insulin processing, resulting in elevated sugar levels that may lead to a range of health complications over the long term. It can affect individuals socially by altering their quality of life, creating an economic impact through the need for insulin and doctor appointments. Hence, the ability to predict the development of diabetes would facilitate early diagnosis and intervention. The main goal is to screen for pre-diabetes, type 1, and type 2 cases and build a high-risk identification system. This work presents a preliminary tackle to screen for type 1, type 2, pre-diabetes, and no diabetes through a comparative analysis of the performance of various trained models using supervised learning in relation to different sampling techniques, including Random Oversampling, Random Undersampling, Synthetic Minority Oversampling (SMOTE), and Adaptive Synthetic Sampling (ADASYN). Our analysis indicates that the K-nearest neighbor algorithm performs best for Type 2 prediction and Naive Bayes for pre-diabetes and Type 1.
Embedded Virtualization on RISC-V with seL4

Embedded virtualization is a key enabler for system designers to meet SWaP-C requirements for mission critical systems that execute various military, aerospace, medical, and commercial applications. The demand is now on the software designer to implement high assurance software that can reliably satisfy safety and time critical requirements. This article presents the first public implementation of the formally proven microkernel, seL4, on the RISC-V hypervisor using H-extension v0.6. 1. This implementation uses the Rocketchip softcore executing in the FPGA fabric of a Zynq UltraScale+MPSoC ZCU102.
Empirical Evaluation of a LoRa Mesh Network for Emergency Communication Systems

Natural and man-made disasters are becoming more prevalent and increasing in danger as climate change continues unabated and resources get scarce. Whether it be flood or terrorism, disasters displace people and it remains difficult to reach those in need when the disaster does strike. For years, mobile phone networks have been integral in responding to emergencies, but they rely on costly infrastructure that is prone to outages or outright attack. We propose a long-range, low-power mesh network infrastructure based on a new networking protocol and embedded software implementation called the ClusterDuck Protocol, that seeks to fill the need of disaster communications telemetry gathering for a fraction of the cost of mobile phone networking infrastructure such as 4G LTE or 5G. While the protocol is simple, it was found to have an inadequate implementation at this stage. Error rates were high and bugs were found in implementation that led to an abnormal amount of time spent processing corrupted data, though power consumption was encouraging in spite of these conditions. We examine the strengths and weaknesses of this new protocol and suggest improvements and transmission strategies to be more successful.
Cyclist Intention Estimation at Signalized Intersections Using Deep Neural Networks

Autonomous driving is gaining attention as a future solution for traffic. One crucial aspect is the interaction between automated vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists, especially at intersections where their paths overlap. This paper proposes an intelligent surveillance system to estimate cyclist intentions at a signalized intersection crossing. The system uses a monocular camera to detect and track cyclists, determining the touch point of the cyclist's front wheel with the ground as their position. It incorporates features like traffic signal state, cyclist speed, and position into a Long Short-Term Memory (LSTM) model for intention estimation. The system's performance was evaluated on a dataset of 35 recorded cyclists, highlighting the significance of the signal state in distinguishing between crossing and stopping intentions.
Implementation of Motion Intention Prediction to Highly Back-Drivable Exoskeleton

A powered exoskeleton is attracting attention to tackle the declining birthrate and aging population. One of the important factors of a powered exoskeleton is back-drivability. Safety can be ensured by improving back-drivability, and we proposed a powered exoskeleton that applies highly back-drivable reduction gearboxes at a high reduction ratio, called bilateral drive gear. The total gear ratio of the proposed exoskeleton is 155.23:1, and the wearer's torque can be estimated without a torque sensor. This paper proposed motion intention prediction using LSTM and examined each model's performance by changing the number of LSTM layers, unit size, and input data. Finally, the experiment was conducted, and 96.12 % prediction accuracy was realized, and the worst delay time for prediction was 305 ms.
Exploring the Impact of Organizing Events to Self-Learning Behavior on an Online Learning Platform

The primary objective of this research is to investigate the patterns of self-learning behavior on an online learning platform and assess the effects of organizing stimulating events for learners. Our data collection focused on three key courses centered around game coding over a period of six months. During this duration, three distinct types of events were conducted: live broadcasts, activities in an auditorium, and onsite workshops. This paper presents the analysis and findings regarding the impact of these events on the learners’ progress and engagement.
On Archie's Electrical Parameters of Sandstone Reservoir with Complicated Pore Structures

Archie formula is the classical theory that describes relationship between resistivity and water saturation(S_w)of hydrocarbon-bearing sandstone reservoir.Its application effect mainly depends on appropriate electrical parameters.Sandstone with complicated pore structures is(charaterized) by low porosity,low permeability,more microporosity,more microfissure and great heterogeneity.Its electrical parameters are evidently different from conventional reservoirs.This paper,based on the core electricity experiment data of low permeability sandstone of Upper Palaeozoic in Eerduos(basin),investigates the effects of complicated pore structures on electrical parameters,analyses the method to improve the precision of water saturation calculation through reservoir classification and(using) alterable m and n parameters.Study shows that 1×10~(-3) μm~2 permeability and 12% porosity are the important key point that distinguishes core electricity characters.The parameters are close to that of Humble formula when the porosity is higher than 12%,while the parameters are special when the porosity less than 12%. The parameter n of shan2 quartz sand in Yulin gasfield,close to 1.0,shows the well growth of tabular throats and good sorting of pore throats.A case study is given in this(paper).
Autonomous Driving with Human Guided Image Feature Extraction

Autonomous driving is currently one of the most popular topics, and computer vision is the key and perception-related technology in this field. Although images have the ability to contain a large amount of crucial feature information, deep learning methods require a significant number of samples, resulting in low data utilization. This paper proposes a learning from demonstration approach that leverages the control laws from human expert drivers to guide a deep convolutional neural network (CNN) in learning features from RGB images captured by the forward-facing camera. Subsequently, the CNN is utilized as a differentiable control layer to obtain an autonomous driving agent with the capabilities of an expert driver. Experimental results of this method are analyzed for designing a lane-keeping control system that learns human driving style via behavioral cloning.
Robust Body Biased Level Shifter

As an integrated circuit, the system on a chip integrates a whole electronic system onto it. The various functional blocks inside the system on a chip may work at different voltage levels. In such multiple-VDD systems, reliable level shifter circuits are necessary for voltage conversion among the various functional blocks of the system on the chip to retain the overall functionality of the system. The proposed Robust Body Biased Level Shifter (RBBLS) uses an error correction circuit and a cut-off transistor to lower the contention and the static current. The RBBLS uses a bias voltage of 0.6 V with a supply voltage of 1.2 V. The circuit is implemented using Cadence Virtuoso at 45 nm technology with an input frequency ranging from 1 Hz to 1 MHz along with the upside voltage conversion range from 0V-0V and 120mV-1.2V. The RBBLS has a delay of 249.8 ns at a 0.01 MHz input pulse frequency (fIN), energy per unit transition of 1.52 pJ, power consumption is 15.22 nW and a power delay product of 3.801 fWsec. The RBBLS shows a drop in delay, power, energy and power delay product by 95.62%, 82.48%, 82.44%, and 99.23%, respectively, as compared with the re-implemented Robust Level Shifter from the literature.