Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical simulations of impulsively excited magnetosonic waves in two parallel solar coronal slabs

Aims. We investigate impulsively excited magnetosonic waves in a finely structured coronal loop that is modeled by a system of two parallel strands. Methods. The equilibrium model is described by the time-dependent ideal magnetodydrodynamic equations that are solved numerically. Results. Numerical simulations reveal that signatures of these waves in wavelet spectra depend on the distance between the strands and on the position of the initial pulse. In all cases, these spectra display complex structures that correspond to short- and long-period waves. We find that, with increasing distance between the strands, short-period waves dominate the wavelet spectra. Conclusions. We demonstrate that the presence of a second strand affects wave propagation compared with one strand alone.
Key Information Extraction from Mobile-Captured Vietnamese Receipt Images using Graph Neural Networks Approach

Information extraction and retrieval are growing fields that have a significant role in document parser and analysis systems. Researches and applications developed in recent years show the numerous difficulties and obstacles in extracting key information from documents. Thanks to the raising of graph theory and deep learning, graph representation and graph learning have been widely applied in information extraction to obtain more exact results. In this paper, we propose a solution upon graph neural networks (GNN) for key information extraction (KIE) that aims to extract the key information from mobile-captured Vietnamese receipt images. Firstly, the images are pre-processed using U2-Net, and then a CRAFT model is used to detect texts from the pre-processed images. Next, the implemented TransformerOCR model is employed for text recognition. Finally, a GNN-based model is designed to extract the key information based on the recognized texts. For validating the effectiveness of the proposed solution, the publicly available dataset released from the Mobile-Captured Receipt Recognition (MC-OCR) Challenge 2021 is used to train and evaluate. The experimental results indicate that our proposed solution achieves a character error rate (CER) score of 0.25 on the private test set, which is more comparable with all reported solutions in the MC-OCR Challenge 2021 as mentioned in the literature. For reproducing and knowledge-sharing purposes, our implementation of the proposed solution is publicly available at https://github.com/ThorPhamlKey_infomation_extraction.
Investigation on novel comparator design

This paper investigates four innovative comparators design and their performance. A Latch-type comparator comprising two cross-coupled inverters is altered for fast operation, with advantages of a high-impedance input, robustness against mismatch, and no static power consumption kept. A splendid technique is a low-power high-precision comparator with an offset scheme implemented on it, achieving a fast and robust offset convergence. The scheme barely introduces delay and mismatch and is designed with minimal extra power consumption and delay. A novel triple-Latch Feedforward (TLFF) fully Dynamic Comparator composed with three stage latches and a feedforward path is proposed to reduce the delay remarkably for large differential input. Another dynamic-bias comparator is designed and compared with prior study, saving the energy consumption considerably with given noise. However, it sacrifices the operation speed due to incomplete discharge. Considerations on the circuit design and performances of the above-mentioned comparators will be discussed in this paper.
GNSS Fault Detection and Exclusion Algorithm for Single Frequency Receivers

Global Navigation Satellite Systems are widely used in numerous industries for positioning, navigation, and timing applications. Nevertheless, there are multiple factors that affect its positioning and dilute its overall accuracy. GNSS performance is characterized by four parameters which include Availability, Continuity, Accuracy, and Integrity. To ensure the best performance, integrity of the system is one of the most important factors to be monitored. In this paper we’ve proposed an integrity monitoring algorithm for single frequency GNSS receivers that detects and identify faulty measurements. Once the faulty satellite is identified, it is excluded from the measurements to ensure reliable outputs. Finally, the results of before and after the exclusion of faulty satellites are compared to ensure positioning accuracy.
Design of a Gamified Training System for Human-Robot Cooperation

Human-Robot Cooperation (HRC) is a field which focuses on employing the best skills of both the robot and the human working together to achieve a common or shared task more efficiently. In most cases, both the human and the robot should know the status of each other and interchange data accordingly. There are several successfully researched HRC systems in the literature proposing solutions to various industrial problems yet few of them are used actively in real-world tasks. One of the major reasons for this gap is that the developed HRC systems do not offer an effective training procedure. The availability of a versatile training setup to get the optimal efficiency is important in addition to evaluating the developed HRC system's usability. Recent studies show that serious games offer effective training outcomes in various sectors such as the military, disaster drills, aviation, health etc. This paper presents an open-source gamified modular training design for HRC applications. It shows how the HRC system can be trained using serious games, what game elements can be utilized and how the learning curve of the user can be measured to evaluate the usability and efficiency. The proposed design is demonstrated through a real-world Cooperative Lifting (co-lift) scenario. The main motivation is to constitute a baseline for effective training of the HRC systems so that the gap between research and successful innovation in the HRC field becomes more narrow.
An Adaptive Overlap-based ICP Algorithm for Multi-LiDAR Calibration in Low-overlap Situations

LiDAR has been widely used as a core sensor in autonomous driving and mobile robotics. Due to the high price of spinning LiDARs, the industry always uses a combination of multiple calibrated solid-state LiDARs. However, there is usually only a small overlap FoV between LiDARs on account of their mounting positions. In such a low-overlap situation, conventional calibration methods always bring catastrophic results. To solve this problem, this paper proposed an adaptive overlap-based ICP algorithm, which can calibrate both 2D and 3D LiDARs. The algorithm adaptively extracts the overlapping point clouds by identifying the correct matching results and constructs an optimized ICP alignment. By controlling the radius range of the point cloud search, our method allows for quick extrinsic calibration of two LiDARs with low-overlap FoV. Experiments show that our algorithm achieves a more robust and accurate low-overlap registration than other methods, and also has speed advantages in general situations.
Autonomous Boundary Detection Using Image Recognition for Robotic Lawn Mower

Most robotic lawnmowers adopt buried metal wires to define their working area, which is labor and time-consuming. This study applies the fusion of multiple sensors, including an inertial measurement unit (IMU), wheel encoders, light detection and ranging (LiDAR), and an RGB-D camera, to automatically detect the boundary of the lawn and construct the navigation map. Visual simultaneous localization and mapping (vSLAM) are adopted based on an RGB-D camera. For the boundary pavement or obstacles lower or similar to grass height, a support vector machine (SVM) is applied to identify boundaries and obstacles and establish the augmented Octomap with virtual walls. A set of image processing methods is proposed to lower the computing load of the embedded system and transfer the image data into point cloud data to construct the navigation map. The robot operating system (ROS)-based control system of the prototype was built using the embedded system (Jetson Nano, NVIDIA; Santa Clara, CA, USA) and control boards (ARM STM32, STMicroelectronics; Geneva, Switzerland). The proposed method could be further used in the process of map construction and navigation.
Study of xenon 4d, 5p, and 5s photoionization in the shape-resonance region using spin-resolved electron spectroscopy

Photoionization of Xe atoms is investigated close to the maximum of the 4d shape resonance by means of spin-resolved photoelectron spectroscopy. Using circularly and linearly polarized synchrotron radiation of 93.8-eV energy, we investigate the photoionization processes of the 5p, 5s, and 4d shells. By combination of earlier data for the partial and differential cross sections with our measurements, we are able to perform a complete characterization of the Xe 4d photoionization process, i.e., we can determine all the relativistic dipole matrix elements. Comparison with different theoretical calculations shows the importance of relaxation effects st this photon energy.
Robust Estimation of the First-Order Autoregressive Parameter

Abstract Outliers in time series can adversely affect both the least squares estimates and ordinary M-estimates of autoregressive parameters. Attention is focused here on obtaining robust estimates of the parameter for a first-order autoregressive time series xk The observations are y k = z k + v k, and two models are considered: Model IO, with v k ≡ 0, x k possibly non-Gaussian, and Model AO, with v k nonzero and possibly quite large a small fraction of the time, and x k Gaussian. A class of generalized M-estimates is proposed which has attractive mean-squared-error robustness properties towards both IO and AO type deviations from the Gaussian model.
Planning Marketing Strategies in Small-Scale Business Using Data Analysis.

The proposed research work develops a system focused on business opportunities to enhance market returns and improve marketing strategies and new strategies by identifying how customers interact with products and their behavior. Existing research efforts attempt to identify and market consumer attraction to products and marketplace in the marketplace. Current research focuses on the challenges of identifying consumer buying patterns and how consumers interact with products, Existing research has not, however, integrated the essential elements into a single system. Consequently, the recommended study has been conducted on a number of significant issues, such as determining the high-value client base and the number of sectors, understanding the purchasing pattern of products that comprise the customers' basket, identifying customer lifetime value, and Customer Trajectory Determination for identifying customer attractive shelf. This system focuses on various machine learning algorithms. Customer segmentation and value analysis using K Mean, Agglomerative, Clustering algorithm, and Arima model. Association rules are generated using the Apriori algorithm for market basket analysis, which is built on the idea that a set of frequently purchased items is a subset of a set of frequently purchased items. Also using RFM analysis to create and prepare our data frame by using BG/NBD model and the Gamma-Gamma model to calculate the customer lifetime value standardization. Using image processing algorithms and retail video analysis algorithms, background reduction technology clearly identifies moving objects/ tracks customer routes using different colors. Based on results from implementation and testing, it was determined that the suggested technique outperformed the use of CCTV to identify consumer behavior and satisfaction with the product in recognizing customer purchasing patterns. The proposed system can identify customers' buying patterns, how customers interact with products, and their satisfaction, and thus focus on business opportunities, improving trade returns, improving marketing strategies, and new strategies.
Machine Learning and Big Data Based IDS System Extensive Survey

Network traffic and logs are monitored by intrusion detection systems (IDS) for abnormal behavior that could indicate a security breach. Traditional IDS techniques require a considerable amount of time when processing big amounts of data. It also complicates the system and diminishes its effectiveness. In addition, the time required to analyze the data renders the system open to attack for an extended period of time before an alarm is generated. IDSs increasingly employ machine learning algorithms to enhance their ability to identify threats in the presence of massive volumes of data. This paper will introduce machine learning and IDS integration for big data. This connection will enhance the IDS system, increase its detection capabilities, and produce more accurate IDS results. A comprehensive survey of IDS that employ machine learning and big data is offered. Comparison is made between several machine learning algorithms used to improve IDS and the feature selection conducted on a specific dataset.
Deterioration-detection and Failure-detection Algorithms for Storage Batteries Using Hysteresis Characteristics

We developed deterioration-detection and failure-detection algorithms for a storage battery and succeeded in declaring the timing of future failure by using the hysteresis characteristics of the storage battery and voltage change of only a few seconds. The greatest advantage of these algorithms is using the change in hysteresis caused by the physical phenomenon of the storage battery, i.e., the change in resistance due to deterioration and difference in thermal reaction energy during discharging. We evaluated storage batteries by linking these hysteresis characteristics with voltage change after charging and discharging. By capturing the difference in or ratio of the voltage change after charging and discharging, we could determine the health and failure potential of a storage battery. For cells with failure potential, we could detect the failure timing six months in advance and we experimentally confirmed that the cell actually failed.
Numerical simulation of nonaxisymmetric adiabatic accretion flow

The hydrodynamics of gas flow past a finite-sized gravitating central object is studied in the adiabatic approximation in two spatial dimensions. The flow morphology is shown to depend on the ratio (epsilon) of the axisymmetric accretion radius to the length scale of the density variation. For small to intermediate epsilon values the flows are highly time dependent, and for large epsilon values a quasi-steady state is obtained. 36 references.
Analysis of Magnetic Field Induced by Cylindrical Electromagnetic Coil for Wireless Actuation of Magnetic Endocapsule Devices

Electromagnetic actuation is an evolving wireless control approach for actuating magnetic endocapsule devices. Electromagnetic actuation is performed by imparting torque and force on magnetic endocapsule devices through remotely induced magnetic fields. This paper presents the analysis of the magnetic field induced by the cylindrical electromagnetic coils for wireless actuation of magnetic endocapsule devices in the gastrointestinal tract. The magnetic field analysis includes how the magnetic flux density is attenuated with the axial distance and how the magnetic flux density and gradient vary with the current at a given axial data point. The magnetic flux density, gradient and driving forces for the actuation of magnetic endocapsule devices, are studied using an analytical model and finite element simulations. The analytical model and finite element simulations were validated experimentally. The weight of magnetic endocapsule lifted by the generated force for different axial data points was assessed.
Research on Full Chain Construction Intelligent Control System and Return Evaluation Model Mathematical Optimization

Aiming at various problems existing in construction projects, this paper constructs a whole-chain intelligent control system for construction projects, and studies the optimal path of the intelligent control system return evaluation model. By collecting a large amount of data generated in the construction management process, in order to improve the control efficiency and accuracy, the construction process data is classified and organically linked with the control process, and a data-driven model is constructed. Describe and model the internal structure and construction process of the whole chain of heterogeneous data of the construction project, and generalize the effect of heterogeneous data to each sub-process, design the UML fusion mechanism, and embed the data-driven UML fusion into the control The whole chain intelligent control system of process and construction projects. Finally, the general return evaluation model is optimized and improved, in order to more accurately evaluate the performance of the whole chain construction intelligent control system in terms of control effect and control income.
Application Analysis of Machine Learning in Intelligent Operation and Maintenance System

The traditional operation and maintenance platform is dependent on the static rules set manually, which can not better cope with the dynamic and complex changing scene. Nowadays, with the rapid development of machine learning and artificial intelligence, intelligent operation and maintenance system can make more efficient and accurate decisions in the face of dynamic changing scenarios through big data accumulated in business scenarios, and can also automatically monitor services, detect abnormal events, and deal with faults in emergency. This paper carefully analyzes the necessity of constructing an intelligent operation and maintenance system, and the application of machine learning in the analysis and fault detection of intelligent operation and maintenance system.
Single-phase VSM based on dq coordinate

Virtual synchronous machines (VSM) is a three-phase grid friendly inverter with widely used in microgrids. This paper proposes a single-phase VSM with a single-phase park transform unit (SPPTU). the SPPTU can perform park transformation on the single-phase current and voltage signals to obtain the dq components of the current and voltage signals and offer a dq coordinate system for the single-phase VSM algorithm. The results after the calculations of the electromagnetic torque Te, active power P and reactive power Q in VSM algorithm based on this dq coordinate system do not have the second harmonic component. Thence, the algorithm in the single-phase VSM with SPPTU can delete the second harmonic filter compared to the old algorithm of single-phase VSM without SPPTU, and improve the control accuracy furthermore. The single-phase VSM is based on paper [5] introduced in Section I with improvements, so it also includes fast current loop proposed by the paper. We present the performance of the novel features of single-phase VSM through a series of simulations. In addition, we also present the design of the current controller. The results show that the single-phase VSM leads to a performance as good as the VSM from [5] in terms of output active power oscillations and stability.
Firefly Swarm Intelligence Based Automatic Clustering and Tracking for UANETs

Subject to high mobility, dynamic topology, and limited energy of unmanned aerial vehicles (UAVs), maintaining stable communication performance is a challenging task in UAV ad-hoc networks (UANETs). As a potential solution, clustering routing algorithm divides the entire network into multiple clusters and various optimal strategies can be adopted to achieve strong network performance. In this paper, we propose a firefly swarm intelligence based automatic clustering and tracking algorithm (FSIACT) for UANETs, which is inspired by the collective behavior of fireflies. Firstly, we propose the fitness function consisting of link survival possibility, average distance and residual energy, and utilize it as the light intensity of the firefly. Secondly, firefly algorithm (FA) is put forward for cluster head (CH) selection and cluster management. Based on the characteristics of the FA, the whole swarm can be automatically divided into several clusters and cluster members (CMs) are willing to track the CH in the cluster. It is verified in simulations that the proposed algorithm achieves the lower handover rate of CHs, longer link expiration time (LET) and longer node lifetime.
Deep Learning based Recognition of the Indian Medicinal Plant Species

Since the dawn of time, medicinal plants, usually referred to as medicinal herbs, have been employed in traditional medicine. Numerous substances are produced by plants for purposes including defense against pests, diseases, fungus, and herbivorous animals. Antiquated Ayurvedic original copies, Egyptian papyrus, and Chinese books portray the utilization of spices. Medicinal plants and herbs have been used as medicines for over 4000 years. Plant parts such as flowers, leaves and roots are highly medicinal. However, it is a difficult process for agronomists and pharmaceutical companies to identify suitable medicinal plants, resulting in the loss of ancient knowledge of Ayurveda. This research work proposes an automated system to identify plants by using a CNN model. To identify rare new species, a thorough understanding of plants is required. Changes in leaf properties are useful for conducting comparative studies of plants. Therefore, this automated system will help to identify medicinal plants and helps agronomists identify the correct herbs.
Disposable Soft Robotic Gripper Fablicated from Ribbon Paper with a Few Steps of Origami Folding

An easy-to-make paper-crafted robotic gripper for food manipulation was proposed. The gripper is fabricated from ribbon paper in a few tens of seconds only by folding and engaging without adhesives. The gripper forms a four-linkage underactuated mechanism which realizes human-finger-like grasping. Simply sliding a gripper edge into a shaft slit of a rotational motor, the gripper is instantly attached and detached from a robot. Therefore, it is expected to realize a practically disposable gripper. By experiments of repeatedly grasping some foods, the reliability and durability of the gripper were shown. The gripper’s spring effect, naturally given by paper material, was shown to hold slippery and irregular-shaped foods firmly.
Optimization Model for Predicting Stored Grain Temperature Using Deep Learning LSTMs

With reliable and accurate grain temperature forecasting models, granary operators could easily make the right decisions to avoid food spoilage. In this study, an analysis of a single hidden layer long short-term memory model, a multi-layer (stacked) long short-term memory model, and its evaluation is presented to determine how accurate it is for forecasting stored grain's temperature from past data. Using temperature sensors, the data is collected over three years in a warehouse in Yunnan, China. There are two datasets: a training dataset and a test dataset. About 40 percent of the data is set aside as a test dataset, while the remaining 60 percent is used as a training dataset. There are several hyper-parameters included in the analysis. By computing the root of the mean square error (RMSE), we can compare the two models. We also use the mean absolute error assessment tool (MAE) and the correlation between predicted and actual values (R2) to evaluate the prediction. In addition to optimizing the number of hidden layers and neurons in each hidden layer, the two models are improved by comparing the actual and predicted models. The experiments we conducted confirm that a single hidden layer can achieve the same or better results than the multilayer (stacked) LSTM when the hyper-parameters are chosen and tuned appropriately, considering the size of the data and the goal.