Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Copy Move Forgery Detection using Guided Filtering and Geometric Invariant Features

Because of the Technical Advancements of many Android applications, image fraud is becoming a vital study problem for many academics. It is a typical threat employed in everyday life to further unlawful goals. Image fraud has been classified into distinct forms based on their forging pattern, with copymove forgery being the most common danger. The identification and localization of precise copy-move image forgeries are two needs of digital forensic technologies. Several computer vision approaches have been developed to identify copy-move forgeries. However, detecting copy-move forgeries with improved accuracy and fewer computing resources remains a research challenge. By deploying the more functional approaches of the computer vision coupled with objective of resolving the above challenge new technique is proposed for the CMFD. Initially on the digital image preprocessing is implemented by lightweight and effective guided filters. The filtered image is further divided into non- overlapping blocks. The moment invariant features are extracted from each block. Those extracted feature values are subjected for lexical sorting. The candidate blocks are selected using the predefined threshold value. and then forged blocks are identified using the Euclidian Distance . After detection of all the forged blocks, we re- arrange the blocks to rebuild the forged detected image as the outcome. The efficiency of the proposed algorithm is analyzed on different datasets and various block size parameters.
Q-Learning Based Optimal Tracking Control of Free-Flying Space Manipulators with Unknown Dynamics

This paper investigates a Q-learning based optimal tracking problem of a free-flying space manipulator with unknown dynamics. First, via a series of equivalent transformations, we obtain a simplified model-free solution form of the original optimal tracking problem. Then we construct a Q-learning based policy iteration mechanism with an actor/critic neural network structure to solve the optimal control policy. By following a Lyapunov analysis we prove the asymptotical stability of the closed-loop system. Finally, a numerical simulation of a 2-DOF space manipulator verifies the effectiveness of the proposed method.
Intelligent detection method of pontoon off-line of cold source interception network based on YOLOv5

Aiming at the problem of the drop in the carrying capacity of the interception net caused by the disconnection of the cold source interception net buoy at the water intake of the nuclear power plant, which affects the safety of cold source water intake. Based on the YOLOv5 target detection technology, this research proposes an intelligent detection method for cold source interception net buoy disconnection, which helps to grasp the position and status of the interception net buoy in real time, and put forward corresponding treatment measures in time. This method is based on deep learning and traditional vision technology, obtains the position of the buoy through the YOLOv5 target detection model, and classifies and judges its status, and detects the abnormality of the early warning buoy out of the network. This method is independently developed based on deep learning networks such as YOLOv5. The test results show that the recall rate of the floater off-net recognition is over 95%. This method can effectively detect the abnormal state of the buoy of the cold source interception network at the water intake of the nuclear power plant, and provides a certain safety guarantee for the cold source water intake of the nuclear power plant.
Finite-time stabilization for high-order nonholonomic systems with full state constraints

This paper studies the finite-time control problem for a class of high-order nonholonomic systems. A new smooth coupling controller is proposed to make the system states converge to zero in finite-time. In order to ensure the states constraints of the nonholonomic systems, a new adding a barrier power integrator technique is presented. The barrier function is integrated into the traditional adding a power integrator technique. Meanwhile, the proposed controller is singular-free and can produce smooth control signals. Finally, simulations verify the obtained results.
Hilbert Transform for Analysis of Amplitude Modulated Wide-band Random Signals

Hilbert transform of the wide-band high frequency amplitude modulation is considered. It is shown that its covariance function is the same as covariance function of the raw signal and cross-covariance functions of them differ only by a sign. It results in an identity of cyclic spectrums of variances for analytic and raw signals. The properties of band-pass filtered signals are examined and it is shown that band-pass filtering can reduce both the number of signal variance cyclic harmonics and their amplitudes. Relationships linking the covariance components of analytical signals and auto-covariance and cross-covariance functions of their quadratures are ascertained.
The interstellar cosmic-ray electron spectrum from synchrotron radiation and direct measurements.

Aims. We exploit synchrotron radiation to constrain the low-energy interstellar electron spectrum, using various radio surveys and connecting with electron data from Fermi-LAT and other experiments. Methods. The GALPROP programme for cosmic-ray propagation, gamma-ray and synchrotron radiation is used. Secondary electrons and positrons are included. Propagation models based on cosmic-ray and gamma-ray data are tested against synchrotron data from 22 MHz to 94 GHz. Results. The synchrotron data confirm the need for a low-energy break in the cosmic-ray electron injection spectrum. The interstellar spectrum below a few GeV has to be lower than standard models predict, and this suggests less solar modulation than usually assumed. Reacceleration models are more difficult to reconcile with the synchrotron constraints. We show that secondary leptons are important for the interpretation of synchrotron emission. We also consider a cosmic-ray propagation origin for the low-energy break. Conclusions. Exploiting the complementary information on cosmic rays and synchrotron gives unique and essential constraints on electrons, and has implications for gamma rays. This connection is especially relevant now in view of the ongoing Planck and Fermi missions.
Imputing missing values for Dataset of Used Cars

Missing values in a dataset has always been a problem for data analysis and modelling. Building a model over a dataset where the missing values are not handled properly will definitely degrade the accuracy and performance of model. This problem particularly impacts deterministic models. Knowing that majority of the models that are used today are deterministic makes dealing with missing values crucial before applying the machine learning model. In this paper we have discussed various approaches such as statistical method (using mean), MICE and KNN for imputing missing values and tested their accuracy in combination with two prediction algorithms linear regression and random forest regression. We have used dataset of used cars containing missing values in few columns to predict the price of car given the details of car and thus comparing the accuracy of the estimated price with different approaches.
Radial pulsations and stability of anisotropic stars with a quasi-local equation of state

Quasi-local variables, i.e. quantities whose values can be derived from physics accessible within an arbitrarily small neighborhood of a spacetime point, are used to construct the equation of state (EoS) for the anisotropic fluid in the spherical symmetry. One parameter families of equilibrium solutions are obtained making it possible to assess stability properties by means of the standard M(R) method. Normal modes of radial pulsation are computed as well and are found to confirm the onset of instability as predicted by the M(R) method. As an example, a stable configuration with outwardly increasing energy density in the core is obtained with a simple quasi-local extension of the polytropic EoS. It is also found that the loss of stability occurs at higher surface compactness when the anisotropy of pressures is present.
Application of Federated Learning in Health Care Sector for Malware Detection and Mitigation Using Software Defined Networking Approach

This research takes us forward with the concepts of Federated Learning and SDN to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital ICEs, the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.
Robust Non-Fragile Control for Uncertain Coupled Parabolic PDE-ODE Systems and Its Application to Hypersonic Rocket Car

This paper introduces a robust non-fragile control for uncertain coupled parabolic PDE-ODE systems. Based on an appropriate Lyapunov function, a robust non-fragile control strategy presented by space-dependent linear matrix inequality-based condition is given such that the closed-loop coupled parabolic PDE-ODE system is exponentially stable. Finally, we verify the effectiveness of the proposed method via a hypersonic rocket car.
A THREE-PHASE CHEMICAL MODEL OF HOT CORES: THE FORMATION OF GLYCINE

A new chemical model is presented that simulates fully coupled gas-phase, grain-surface, and bulk-ice chemistry in hot cores. Glycine (NH2CH2COOH), the simplest amino acid, and related molecules such as glycinal, propionic acid, and propanal, are included in the chemical network. Glycine is found to form in moderate abundance within and upon dust-grain ices via three radical-addition mechanisms, with no single mechanism strongly dominant. Glycine production in the ice occurs over temperatures ∼40–120 K. Peak gas-phase glycine fractional abundances lie in the range 8 × 10−11–8 × 10−9, occurring at ∼200 K, the evaporation temperature of glycine. A gas-phase mechanism for glycine production is tested and found insignificant, even under optimal conditions. A new spectroscopic radiative-transfer model is used, allowing the translation and comparison of the chemical-model results with observations of specific sources. Comparison with the nearby hot-core source NGC 6334 IRS1 shows excellent agreement with integrated line intensities of observed species, including methyl formate. The results for glycine are consistent with the current lack of a detection of this molecule toward other sources; the high evaporation temperature of glycine renders the emission region extremely compact. Glycine detection with ALMA is predicted to be highly plausible, for bright, nearby sources with narrow emission lines. Photodissociation of water and subsequent hydrogen abstraction from organic molecules by OH, and NH2, are crucial to the buildup of complex organic species in the ice. The inclusion of alternative branches within the network of radical-addition reactions appears important to the abundances of hot-core molecules; less favorable branching ratios may remedy the anomalously high abundance of glycolaldehyde predicted by this and previous models.
Research on Key Node Identification Method of Transmission Network based on Improved PageRank Algorithm

In order to effectively identify the key nodes before power grid failure, an improved PageRank algorithm is proposed to identify the key nodes in power system. Firstly, establish undirected network, introduce power flow and establish network model. Secondly, the node types are divided and the transmission matrix is established. Finally, according to the equivalent impedance value, the Google electric matrix is established to form an improved PageRank algorithm to identify the key nodes in the power system. The simulation results of IEEE 39 bus system show that this method is feasible and effective in terms of load loss and transmission capacity.
An Air Substrate Microstrip Patch Antenna for N77 Band Application

A low cost compact microstrip patch antenna is developed using air as substrate. The antenna is fed diagonally using a coaxial cable. The antenna has a VSWR <2 from 3.23 GHz to 4.2 GHz. Air has been used to cut down the cost of the antenna and to make the antenna more rugged the patch is made of 1 mm thickness. The maximum gain varies between 8.1 dBi to 9 dBi. The antenna is designed and simulated in CST microwave studio.
Deep Reinforcement Learning Based Autonomous Control Approach for Power System Topology Optimization

With the rapid development of modern power systems, the penetration rates of power electronic equipment and renewable energy are increasing, which bring challenges to stable operation. Due to the strong randomness and uncertainty, the conventional method based on mathematical model can not control modern power systems. So in this paper, a deep reinforcement learning (DRL) based method is proposed to realize the stable autonomous control of power systems. Specific, soft actor-critic (SAC) is used to reroute power flow in transmission lines via autonomous topology optimization control. Besides, to solve huge action space in topology switching and the vulnerability of DRL agents in power systems, a pre-trained scheme based on imitation learning (IL) is presented to use in SAC. Simulations on the IEEE 118-bus system for topology optimization are carried out under random perturbations and common adversarial perturbations to validate the effectiveness and robustness of the proposed methods. The results show that our methods have outstanding performance.
Tea Bud Recognition Based on Machine Learning

Due to the low efficiency and high cost of manual tea picking, as well as the development and application of machine vision and image recognition technology, the mechanization and intelligence of tea picking will become a trend. First of all, this paper analyzed the advantages and disadvantages of several common target detection methods by using Matlab simulation. Then, considering cost, accuracy and real-time performance, the method of combining K-means clustering and image morphology processing is finally selected to extract tea buds. Next, the method is reproduced on STM32 single chip microcomputer. Finally, the effect was verified in the actual tea garden, which laid a foundation for the subsequent intelligent picking.
Automatic Detection of Heart Valve Disorders Using Hybrid Signal Processing and Convolutional Neural Networks

The aim of this work is to design a reliable algorithm for the automatic detection of heart valve disorders (HVDs) without any segmentation of the heart sound signals. Teager-Kaiser energy operator (TKEO) and rational dilation wavelet transform (RDWT) are utilized to extract representative features in order to detect abnormal patterns in PCG signals with the employment of deep learning model. First, TKEO is used to extract the instantaneous energy of the source that generates the Phonocardiogram (PCG) signal rather than the energy of the signal itself. Then, RDWT is employed to decompose the instantaneous energy of the PCG signal into different sub-bands. The oscillatory characteristics of PCG has been retained in these sub-bands, which are served as discriminant features. Third, these features are fed to one-dimensional (ID) convolutional neural networks (CNN) for classification. Finally, experiments including two types of classification named binary classification and multi-class classification, are carried out on a well-known and publicly available PCG database to verify the effectiveness of the proposed method. The overall average accuracy for binary (5 cases), four-class and five-class classification is reported to be 100%, 99.00%, 99.75%, 99.75%, 99.60%, 98.87% and 98.10%, respectively. The proposed method has obtained superior accuracy in comparison to most of the state-of-the-art approaches using the same database.
Benefit Allocation of Maize Industrial Chain Coalition Based on Modified Shapley Value

It is important to fairly allocate revenue to promote the integrated development of the whole industrial chain of agricultural products and to promote the sustainable and healthy development of the agricultural industry. This paper investigates the maize industry chain, which is composed of farmers-processors-logistics enterprises as the object, and takes resource contribution, cost degree, risk resistance and ecological devotion into consideration as factors influencing the distribution of benefits. The results show that farmers, as maize producers, are dominant in the maize chain, so their ultimate benefits are higher than those of other agents. With the improved Shapley method, the benefits can be distributed more fairly and cooperative alliances can be encouraged. Moreover, the overall benefits of the maize industry chain are greatest when all the maize industry chain players participate in full cooperation.
Global Registration of Point Cloud Maps with Low-overlap Regions

Merging multiple maps is necessary when the task of mapping a large area is divided and assigned to different robots. Unlike scanned or reconstructed models, point cloud maps built by SLAM are usually sparser and contain much noise, making current global registration methods failed. In this paper, we focus on the global registration of point cloud maps which have low-overlap regions and no prior relative transformation between each other, so that maps can still be merged in GPS-denied environment. Our method adopts a pipeline of feature matching, where key points are first selected evenly within voxel grids and then used for getting feature descriptors. After matching for correspondences, a RANSAC based registration gives the rough transformation between maps, which can further be refined by ICP. To improve the accuracy of correspondences, we specifically introduce a keypoint clustering and classification method. Except for 3D point cloud maps as inputs, no other information is needed. Experiments on the campus show that our approach successfully deals with large area with similar structures.
Screening of Key Genes for Triple-Negative Breast Cancer Based on a Differential Co-expression Method

The screening of key genes in triple-negative breast cancer is mainly studied in this paper. A diagnostic model based on the differential co-expression network method DiffCoEx is investigated. The result of commonly used differential coexpression analysis are modules composed of a large number of genes, with no further modeling methods provided to narrow down the gene modules for deeper exploratory analysis. The purpose of this study is to establish a diagnostic model that combines differential co-expression network and Lasso regression to screen key genes of triple-negative breast cancer. Data sets were collected including 36 cases of triple-negative breast cancer and 36 cases of non-triple-negative breast cancer. Five key genes of triple-negative breast cancer were obtained by using the adjacency matrix, topological overlap, hierarchical clustering, and sequencing test constructed by the differential co-expression network analysis DiffCoEx and modeling and screening by the Lasso regression. The reliability and validity of the model and index genes are demonstrated by the area under the subject working characteristic curve of the verification set of 67 samples.
Sparse Identification of ARX Model Based on $L_{p}$–Regularization

This paper studies the sparse identification problem of ARX model by using the $L_{p}$-regularization method with $0 < p < 1$. We construct the information criterion which is a linear combination of the prediction error with the $L_{p}$ - regularization term where the coefficient can be adaptively adjusted. By minimizing the information criterion, we propose a sparse parameter identification algorithm based on $L_{p}$-regularization. We design the adaptive step size by using the observations and obtain the parameter convergence and set convergence of the proposed algorithm. Finally, we give a simulation example to show that the $L_{p}$-regularization algorithm proposed in this paper has better performance than the $L_{1}$-regularization algorithm and the recursive least squares.
Photovoltaic power prediction based on SVMD-PCA-EL model

With the rapid development of photovoltaic (PV) power generation, PV power prediction has received considerable attention. This study presents a novel PV prediction model based on successive variational mode decomposition (SVMD), principal component analysis (PCA) and ensemble learning (EL). During the data preprocessing phase, SVMD is used to decompose the environmental variables and PV power into different frequency components, and PCA is utilized to obtain the key influencing components of environmental components to reduce the dimension of data set. Secondly, the EL strategy is adopted to predict each power components. Four base learners, i.e. BP-Adaboost, RVM, ELM and RF, are selected and prediction results of base learners are linearly combined using the teaching-learning-based optimization algorithm. Then, the final prediction result is obtained by adding up all individual component prediction results. Results show that the proposed model has better prediction accuracy than any base learner. Moreover, the effectiveness of SVMD and PCA is demonstrated by comparison analysis.
Path Following Controller for Snake Robots Based on Data-driven MPC and Extended State Observer

This paper investigates the straight path following control problem for snake robots without any prior model knowledge. A data-driven model predictive control (MPC) scheme is designed based on a data-driven Koopman model and an extended state observer (ESO). Firstly, this paper establishes a data-driven model for heading control of the snake robot based on Koopman operator theory. The Koopman operator enables global linear representations of nonlinear dynamical systems in a high-dimensional space. Then, a MPC is designed based on the high-dimensional linear Koopman model, which can solve the optimal control input that satisfies the constraints in real-time. Moreover, the model uncertainty is estimated by ESO and compensated in the controller, which improves the robustness of the controller. Finally, the effectiveness and superiority of the proposed controller are verified by simulations.