Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term wind speed prediction method based on deep learning

Wind power is widely used in industrial power generation. Whether wind speed can be accurately predicted is of great significance to the stable operation of the power system. This study is proposed based on the combination of the SSA-RNN wind speed forecasting model and singular spectrum analysis (SSA) algorithms for preprocessing of wind speed. It can decompose the trend from the sequence of wind speed, oscillation, and noise, reduce the volatility and the stability of the wind speed, cycle through a neural network (RNN) to forecast the subsequence after decomposition, and obtain a good prediction accuracy. The experimental results show that this method can effectively improve the utilization of wind energy resources and help the operation, scheduling, and control of industrial power systems.
FLAVOR GONIOMETRY BY PROTON DECAY

Unification of strong and electroweak forces implies that protons (and bound neutrons) decay. Modes like . nu. -bar, e/sup +/. pi. , and . mu. /sup +/K/sup 0/ are expected, while modes like . mu. /sup +/. pi. and e/sup +/K/sup 0/ are ''Cabibbo'' suppressed. Branching ratios can reveal much about the nature of the unifying group and the origin of fermion masses. Plausible models of unification and flavor mixing give surprisingly different predictions for two-body branching ratios.
Cooperative Spectrum Sensing Algorithm for UAV Based on Deep Learning

The cooperative working mode of multi-UAV (Unmanned Aerial Vehicle) has an important application prospect in the future communication. Considering the shortage of spectrum resources, a cognitive UAV network model is established, and the performance of multi-UAV cooperative spectrum sensing is studied. For the large cognitive UAV network with a large number of UAVs, a high accuracy cooperative spectrum sensing algorithm is proposed, and the performance of the algorithm, support vector machine and K-out-of-N algorithm in Rayleigh fading channel is compared. Simulation results show that the performance of the proposed algorithm is better than the other two algorithms in Rayleigh fading channel, and has better performance in low signal to noise ratio (SNR).
Whole Body Collaborative Planning Method for Legged Locomotion Manipulation System in Operation Process

The legged locomotion manipulation system (LMS) combines the powerful movement of the legged platform and the operation ability of the robotic manipulation arm, which can replace humans to perform dangerous tasks. This paper focuses on the legged locomotion manipulation system, composed of a quadruped locomotion platform with a six-degree-of-freedom (Dof) robotic arm. After the LMS completes movement tasks and reaches the designated position, the supporting foot point are not moved. The overall kinematics model is established with the parallel kinematics model of the legged LMS and the serial kinematics model of the robotic arm. In whole-body collaborative planning, the goal is to minimize the sum of the squares of all joint torques. With the constraints of whole-body kinematics and statics, dynamics, and stability, the trajectory optimization problem is converted into a nonlinear planning problem. By solving the nonlinear problem, we can get the optimal joint trajectory. Compared with independently planning trajectories of the quadruped locomotion platform and robotic arm, this algorithm can coordinate the motion of the whole joints, optimize the distribution, and reduce the system's power. Simulation experiments verify all algorithms.
Efficient Pareto Optimality-based Task Scheduling for Vehicular Edge Computing

Vehicular Edge Computing is a promising paradigm that provides cloud computing services closer to vehicular users. Vehicles and communication infrastructure can cooperatively provide vehicular services with low latency constraints through vehicular cloud formation and using these computational resources via task scheduling. An efficient task scheduler must decide which cloud will run the tasks, considering vehicular mobility and task requirements. This is important to minimize processing time and, consequently, monetary cost. However, the literature solutions do not consider these contextual aspects together, degrading the overall system efficiency. This work presents EFESTO, a task scheduling mechanism that considers contextual aspects in its decision process. The results show that, compared to state-of-the-art solutions, EFESTO can schedule more tasks while minimizing monetary cost and system latency.
An Automated Fish Species Classification System Using Improved Alexnet Model

Due to the extremely dark nature of the sea's inner water and the fish's quick movement, classifying fish species from images obtained from the ocean presents significant challenges. This article describes an automated approach for identifying and classifying fish species using the deep learning method. It benefits marine scientists in various ways, most notably by allowing for the accurate monitoring of fish reproduction, development, and marine changes. AlexNet, a popular deep convolutional neural network model, is employed in this proposed study to classify fish species. This research modifies the traditional alexnet design to improve the accuracy of fish classification. In this proposed AlexNet architecture, five convolutional layers are used for an efficient texture and color feature extraction process. In addition, three fully connected layers are used for feature selection and classification. Finally, the classification efficiency of the proposed fish species classification system has been proven by comparative analysis with the most popular deep learning models (Alexnet, GoogleNet and VGGNet). The overall performance of the proposed deep learning model is 94%, its sensitivity is 95%, and its specificity is 95.5%, respectively.
Efficient electrothermal actuation of multiple modes of high-frequency nanoelectromechanical resonators

The authors observed resonances from multiple vibrational modes of individual silicon-carbide-based nanomechanical resonators, covering a broad frequency range from several megahertz to over a gigahertz. The devices are actuated thermoelastically in vacuum at room temperature using localized Joule heating in a device-integrated metal loop. Their motion is detected piezoresistively using signal downmixing in a similarly integrated metal piezoresistor. The frequencies and amplitudes of the observed resonant peaks are in good agreement with the results from theoretical modeling and finite-element simulations.
An Extensive Survey on Recent Advancements in Human Stress Level Detection Systems

The psychological stress has spread to harm all people in recent days. When someone is subjected to high stress continually, it can have a significant impact on their work, academic, and health aspects. To minimize the negative effects of high levels of psychological stress, it is essential to monitor and manage stress systematically. Thus, it is essential to develop reliable techniques for the quick and precise identification of human stress. Though stress detection research has gained worldwide attention by utilizing physiological and social media data, there are still a number of issues that needs to be addressed. This paper aims to discuss the need for Human Stress Level Detection System (HSLDS) focusing on the recent research utilizing human physiology, physical characteristics, social media posts, and micro-blogs. The paper explores a variety of deep learning and machine learning models deployed for stress detection, publicly available datasets based on social media posts and healthcare data, self-reporting surveys, attributes, subjected users, and levels of stress/ emotions categorized. The in-depth investigation in this research reveals new avenues for future research.
Fish Swing Biomimetic Magnetically Controlled Microrobot

Inspired by the swing propulsion of fish, this paper presents a rod-shaped microrobot consisting of a magnetic head, a magnetic tail, and a flexible body. The proposed microrobots can move with great stability and precision, producing fish-like swing motions driven by an external oscillating magnetic field. Microrobots can achieve precisely targeted motion along predetermined trajectories, showing the potential for future in vivo targeted therapy. In addition, we also designed a mechanical-electromagnetic hybrid actuation system suitable for rod-shaped microrobot driving. The simple structure and novel design of the system not only provide the required magnetic field for the microrobot but also benefit the microrobot rotation. The newly designed microrobot and the magnetic actuation system provide a new way for the development of magnetic microrobot.
Convolutional Neural Network Based Detection of Seasonal Features in Time Series Data

Seasonal features of various kinds of time-varying nodes in large-scale complex networks could facilitate the effective network optimization. It is necessary to find the nodes with seasonal features from the limited labeled training set. In this paper, we proposed a sequence convolutional neural network model to detect the seasonal features in time series data, as well as the method of time series upscaling and subsequence embedding as data preprocessing. Experimental results show the effectiveness of our proposed model.
Distributed Formation Control of Second-order System with Input Saturation and Obstacle Avoidance

This paper studies the formation control of second-order systems with obstacle avoidance. The control input is subject to a priori saturation constraint. Two distributed for-mation protocols are proposed. The first one considers the issue of formation with a group reference velocity under undirected graphs. The second one deals with the case that only a subset of systems have access to the leader's information under directed graphs, and the upper bound of the protocol is independent of the number of its neighbors. Explicit Lyapunov functions are constructed to prove the formation properties of the resultant closed-loop system. An algorithm based on the improved artificial potential field is developed to avoid obstacles in the formation process. Numerical simulations are presented to illustrate the effectiveness of the theoretic results.
The Stress Analysis of Moisture-Thermal Coupling Based on an Advanced 3D Packaging

The three-dimensional (3D) chip stacking package provides numerous advantages to 3D plastic packaging. However, moisture and thermal can pose a risk to the integrity of the package. Hence, it is critical to examine the effects of hygrothermal stress allocation on the reliability of 3D chip stacking packages. Based on the previous work, this paper proposes an innovative 3D chip stacking package structure to study the influence of hygrothermal coupling factors on its reliability. The evolution of packaging structures relies on the continual evolution of the packaging process. This study’s new development structure has not been adequately investigated, particularly in hygrothermal coupling. The constructed 3D stacked package chip is hygrothermal simulated in this work using ANSYS finite element simulation software. Moisture and thermal stresses were coupled. The two were then compared to verify that neither moisture nor thermal stresses are negligible. These packages are subject to the most significant reliability risk impact at the solder ball corners. Thermal moisture stress is more prominent in the lower solder ball than the upper layer and the outer ring than the inner. In this research, the ANSYS tool is used for precise modeling simulation and assessment, providing support and a reference for the smooth simulation of wet thermal coupling in innovative package structures. In addition, the impact of different solder ball sizes on the package is investigated from a practical standpoint. The results provide significant guidance for designing this package’s structure and add to the reliability of the design.
Single photon emission from individual GaAs quantum dots

We report on photon correlation measurements on a single quantum dot formed at fluctuations of the interface of a GaAs/GaAlAs quantum well. We demonstrate that under pulsed nonresonant excitation, the quantum dot emits a single photon per pulse. This shows that after the photon emission, there is no refill of the quantum dot by the nearby two-dimensional reservoir of delocalized states. The possibility of delivering Fourier transform limited single photons makes this system a good candidate for exciton- and photon-based quantum information processing schemes.
Primary and Secondary Controls with Reactive Power Sharing in Mesh Microgrids

In microgrids, power sharing represents an important research topic since it prevents inverters from overloading. In fact, several power sharing methods have been proposed in literature. Most of them are dedicated to mono-PCC microgrids only, it means that effectiveness of these methods may not remain in the case of multi-PCC microgrids. That's why, in this paper, a mesh multi-PCC microgrid is considered and a proper power sharing method is suggested. However, even though accurate active and reactive power sharing are achieved, frequency and voltage deviations occurs. Therefore, power quality problems such as negative effects on the power system may result. To solve this issue, a secondary control method is introduced to restore voltage and frequency to the nominal values without compromising the power sharing among DGs. To verify the efficiency of the proposed controls, simulations are conducted using Simscape MATLAB/Simulink environment.
Role of free volume on breakdown of epoxy nanocomposites: measurement and manipulation

Free volume $(f_{v})$, which is strongly associated with the chain mobility and charge transportation in polymer dielectrics, could significantly affect electrical properties. In this paper, free volume of epoxy nanocomposites was effectively reduced by incorporating organically modified $\mathbf{nano}-\mathbf{Al}_{\mathbf{2}}\mathbf{O}_{\mathbf{3}}$ fillers. Consequently, the highest DC breakdown strength (159.09 kV/mm) was obtained when the filler content was 1 wt%. Fourier transformed infrared spectroscopy (FTIR) results indicated that the filler-matrix interaction brought by the formation of hydrogen bonds as well as covalent bonds were responsible for the free volume manipulation. Images of fracture morphology and analysis by small angle x-ray scattering (SAXS) showed that different degrees of filler-matrix interaction existed when incorporating fillers with varied contents, which as a result gave rise to the successful tailoring of free volume. This paper provides feasible methods for the quantitative measurement of free volume as well as its effective manipulation.
Dual manipulator collaborative shaft slot assembly via MADDPG

The traditional dual manipulator control systems have not only complex motion coupling problems, but also larger computational burden, and hence it is difficult to meet the requirements of intelligent assembly. In this paper, based on multi-agent reinforcement learning theory, Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is investigated in the collaborative assembly shaft slot assembly via dual manipulator system. For the collaborative shaft slot assembly in the dual manipulator system, sparse rewards in traditional multi-agent reinforcement learning often exist because of the long sequence decision-making problem. For the above problems, this paper considers the influence of the decision-making of a single manipulator on the overall task rewards when the overall rewards of multi -agent reinforcement learning are designed. In the proposed algorithm, by calculating the difference before and after the state of each manipulator, and applying the difference as the internal state excitation to the overall task rewards, the traditional reward function of multi-agent reinforcement learning is improved. In order to verify the designed algorithm, the dual manipulator shaft slot assembly system and test scenario are established on the CoppeliaSim simulation platform. Simulation results show that the success rate of the shaft slot assembly via the improved MADDPG algorithm is about 83 % *
Global Homography Motion Compensation for Versatile Video Coding

In Versatile Video Coding (VVC), local affine motion compensation (LAMC) is adopted to handle complex motions, such as rotation and zooming. However, it is inefficient to use LAMC to handle the global motion due to the following two reasons. First, the use of LAMC may lead to some extra bit cost on the affine motion model parameters. Second, the precision of LAMC is restricted by the MV precision of the control points. Therefore, in this paper, we propose a global homography motion compensation (GHMC) framework to better characterize the global motion. For each coding block, an extra mode is added to perform motion compensation based on an 8-parameter global homography motion model. In addition, an extrapolation scheme is designed to derive the parameters from reference frames to save the bit cost for signaling them. The proposed framework is implemented into the VVC reference software VTM-6.0. Experimental results show that, on average, 0.69% and 0.66% BD-rate reduction is achieved under Low Delay P and Low Delay B configurations, respectively, for sequences with rich complex global motions.
Micro-Expression Recognition by Combining Progressive-Learning Intensity Magnification with Self-Attention-Convolution Classification

How to detect the subtle intensity change and find the inherent relation of feature maps in a face image is the two key issues for micro-expression recognition. In this study, in order to cope with the subtle intensity change, we design an innovative Cascade Micro-Expression Magnification Network (Cascade-MEMN) to magnify the low amplitude of the intensity change for learning the intensity progressively, which helps suppress overlapping artifacts and produce more realistic magnification. In order to find the inherent relation of facial feature maps, we design a Self-attention-based Convolution Layer (SCL), which introduces self-attention to every pixel when calculating the weights in the sliding window, considering that the variation of micro-expressions is very subtle and local on face. Concretely, the SCLs are used to replace 3 Resblocks from the last stage of ResNet50 with 3 SCL blocks. Finally, a new micro-expression recognition system is realized by combining the progressive-learning-based intensity magnification network with the modified self-attention-convolution classification network. Consequently, the proposed method achieves competitive results for the composite database evaluation (CDE) protocol from MEGC 2019, as shown in the experimental result.
Multivariate statistical analysis for geographers

I. INTRODUCTION AND REVIEW. 1. Elementary Statistics Background. 2. Information Content in Geo-Referenced Data. 3. Introduction to Matrix Algebra. 4. Multiple Linear Regression Analysis and Correlation Analysis. II. INSTANCES OF THE GENERAL LINEAR MODEL. 5. Multivariate Analysis of Variance. 6. Principal Components and Factor Analysis. 7. Discriminant Function Analysis. 8. Cluster Analysis. 9. Canonical Correlation Analysis. III. NONLINEAR AND CATEGORICAL DATA MODELING. 10. Nonlinear Regression Analysis. 11. Spatial Autoregressive Analysis. 12. Special Nonlinear Regression Applications in Spatial Analysis. Epilogue. Appendices. Index.
Heterogeneous Mean-Field Multi-Agent Reinforcement Learning for Communication Routing Selection in SAGI-Net

The utilization of heterogeneous end devices such as the low earth orbit (LEO) satellite, unmanned aerial vehicles (UAVs) and ground users (GUs) deployed at different altitudes, known as the space-air-ground integrated network (SAGI-Net), can be quite promising towards a bunch of advanced applications. Whereas, the energy efficiency of the SAGI-Net communication system is a key criterion needed to be improved urgently in consideration that the inappropriate communication routing will undoubtedly cause a huge communication energy cost of the system especially with a large number of communication devices inside. In this paper, we proposed a novel communication routing selection model for the SAGI-Net system and established a heterogeneous multi-agent reinforcement learning (HMF-MARL) framework to optimize the communication energy efficiency of this system, where the mean-field theory was introduced to enhance the ability of classic MARL method while still maintaining a relatively low computational complexity. The experiment results show that the capacity of the heterogeneous multi-agent system has been improved by nearly 80% using the proposed HMF-MARL method compared with the classic MARL one, which hopefully shows the potential value on the implementation of the SAGI-Net system in the future.
On curvature element-size control in metric surface mesh generation

Summary A new procedure is suggested for controlling the element size distribution of surface meshes during automatic adaptive surface mesh generation. In order to ensure that the geometry of the surface can be accurately captured, the curvature properties of the surface are first analyzed. Based on the principal curvatures and principal directions of the surface, the curvature element-size requirement is defined in form of a metric tensor field. This element-size controlling metric tensor field, which can either be isotopic or anisotopic depends on the user requirement, is then employed to control the element size distribution during mesh generation. The suggested procedure is local, adaptive and can be easily used with many parametric surface mesh generators. As the proposed scheme defines the curvature element-size requirement in an implicit manner, it can be combined with any other user defined element size specification using the standard metric intersection procedure. This eventually leads to a simple implementation procedure and a high computational efficiency. Numerical examples indicate that the new procedure can effectively control the element size of surface meshes in the cost of very little additional computational effort.
Random Access Modelling and Performance Analysis for the 802.11ax UORA Mechansim in Multiple BSSs

IEEE 802.11ax standard aims to improve performance in scenarios with densely deployed access points (APs) and stations (STAs). It provides an uplink OFDMA-based random access (UORA) mechanism which allows the STAs that failed to report their butter status to the AP to transmit data, or the unassociated STAs to transmit an association request frame. Existing studies on UORA performance analysis focus on the analytical model of contention of resource units (RUs) within single basic service set (BSS), which is only in frequency domain. However, in high-density scenarios, where multiple BSSs may locate within the interference range of each other and work on the same channel, the performance of UORA not only relates with the contention in frequency domain, but also associates with the contention in time domain among multiple APs or legacy STAs working on the same channel. As the UORA mechanism is based on the successful transmission of trigger frame (TF), the transmission of TF and UORA process should be analyzed as a whole system. In this paper, we combine the transmission of TF and UORA process, and build a systematic theoretical model to practically characterize the performance of UORA in multi-BSS scenario. The average throughput and successful probability of UORA are derived. In addition, we validate our analysis through extensive simulations.