Miracle-dz/UniFAR-FTUs huggingface dataset:
Formation control design of Multiple Underwater Vehicles based on LADRC

Aiming at the problem of small ocean detection range and low efficiency, this paper applied the cooperative formation mode of multiple autonomous underwater vehicles to study the control effect of traditional PID control and linear active disturbance rejection controller (LADRC) on the formation control problem. Simulation results show that under a certain external disturbance, the PID controller and the LADRC can achieve good control for multiple autonomous underwater vehicles formation, but LADRC algorithm in terms of the performance of disturbance resistance and the quickness of the response of the system is superior to the classical PID control. It can estimate and compensate the total disturbance of the system, so as to improve the anti-interference ability and robustness of the system.
IT2FNN-Based Mode-Asynchrony Variable-Structure Control Under Event-Triggered Communication

This paper deals with the problem of mode-asynchrony sliding-mode variable-structure control (VSC) under event-triggered communication for continuous-time Markov jump systems with hidden modes. The hidden-mode Markov jump systems with both the matched and mismatched uncertainties are formulated. In terms of the event-triggered communication from the sensor to the controller, static and dynamic mechanisms based on periodic/aperiodic sampling are designed for mitigating the burden of communication. In this case of mode-asynchrony and aperiodic sampling state measurements, new sliding-mode VSC laws integrated with an interval type-2 fuzzy neural network are designed. The interval type-2 fuzzy neural network is used to approximate matched uncertainty. Moreover, the analysis of the finitetime practical stability of the control system is presented based on the Lyapunov function approach. Finally, a set of simulation results are given to show the validity and practicability of the present results.
Prospects for decadal climate prediction in the Mediterranean region

The Mediterranean region stands as one of the most sensitive to climate change, both in terms of warming and drying. On shorter time‐scales, internal variability has substantially affected the observed climate and in the next decade might enhance or compensate long‐term trends. Here we compare the multi‐model climate predictions produced within the framework of the CMIP5 (Coupled Model Intercomparison Project Phase 5) project with historical simulations to assess the level of multi‐year climate prediction skill in the Mediterranean region beyond that originating from the model accumulated response to the external radiative forcings. We obtain a high and significant skill in predicting 4‐year averaged annual and summer mean temperature over most of the study domain and in predicting precipitation for the same seasons over northern Europe and sub‐Saharan Africa. A lower skill is found during the winter season but still positive for temperature. Although most of this high skill originates from the model response to the external radiative forcings, the initialization contributes to the temperature skill over the Mediterranean Sea and surrounding land areas. The high and significant correlations between the observed Mediterranean temperatures and the observed Atlantic multidecadal oscillation (AMO) in the summer and annual means are captured by the CMIP5 ensemble which suggests that the added skill is related to the ability of the CMIP5 ensemble to predict the AMO. Such a link to the AMO seems restricted to western Africa and summer means only for the precipitation case.
Machine Learning-based Multi-classification for First-Episode Schizophrenics, Ultra-high risk Schizophrenics, and Healthy Controls

Schizophrenia is a severe chronic disabling disease. Prompt treatment of ultra-high-risk individuals in the prodromal phase is of great significance for preventing the development of schizophrenia. The purpose of this study is to find a way to effectively distinguish ultra-high-risk individuals with schizophrenia, and to analyze important biomarkers of schizophrenia. There are 101 first-episode drug-naive schizophrenia patients, 49 ultra-high-risk individuals and 94 healthy people participated in our study. The cognition data, cortical thickness and the local gyrification index of these participants were collected for the identification of schizophrenia using various machine learning methods. Meanwhile, biological markers that indicate mental illness are identified by analyzing their relationship among different categories of individuals. Support vector machine performed best among the machine learning methods, with a classification accuracy of 86.4%. And the results indicate that the critical features for the identification of the three-type subject are executive function, the right cingulate gyrus, and the left temporal pole.
Analysis of Machine Learning and Deep Learning Intrusion Detection System in Internet of Things Network*

As technology advances, more demands in various sectors rise, making everything smarter. The Internet of Things (IoT) connects everything through an interconnected nodes also bringing improvements to many industrial or manufacturing industries, healthcare sectors, smart retail, agriculture etc. by pushing many more applications to their limits but are vulnerable to threats and attacks. As a result, security and privacy are required to make IoT secure. This article discusses vulnerabilities in different layers of IoT architecture where security is critically required, the use of Intrusion Detection System in IoT networks and how machine and deep learning will help to overcome those assaults. The following paper also discusses the different types of Intrusion Detection datasets along with the issues and challenges in IoT.
Analytical Time Domain Solution for Electromagnetic Field on the Interface of Two Media

The problem of irradiation of the plane interface between two media by impulse electromagnetic wave is considered. The incident wave is radiated by a source in the form of a plane non-stationary electrical current. As example the case of uniform distribution of current and step like time dependence is represented. Using the method of evolutionary equations the incident, reflected, and refracted waves are generated medium modal basis method is presented in form of modal expansion in cylindrical coordinate set. Applying the boundary conditions for the fields the analytical form of the solution is obtained. The matching of the fields is carried out by separation of the amplitude of incident field into two parts with coefficients dependent on material parameters of media.
Micro-expression Recognition Based on Deep Mutual Learning Network

Facial micro-expressions, indicating brief and subtle facial movements, are challenging to analyze. How to fully explore the discriminant and subtle features with only a limited number of samples for deep learning training has not been well studied. To leverage knowledge acquisition in deep learning models, this paper adopts the strategy of deep mutual learning to guide the interactive training of micro-expression sequences between different modes. This method uses the lightweight network Resnet-18 which is suitable for small-scale database as the backbone network. Then, the dual-stream structure is introduced to explore the multi-mode representation for ME recognition. Specifically, the main network is based on optical flow and the auxiliary network is based on RGB sequence. In this method, the difference image based on key frame is used to obtain the relevant feature representation of the micro-expression input by the module. In the training stage, the training process is optimized by mutual learning loss (including supervised learning loss and interaction loss), so that the prediction of each model is consistent with the real information of the training sample and the prediction of the other model. In the testing phase, we use optical flow to test separately to enhance the recognition ability of optical flow branches. Therefore, we can prune RGB branches on the premise of ensuring the accuracy to improve the recognition speed. The experimental results on public micro-expression data sets show that the proposed method can effectively improve the recognition accuracy and the overall performance is better than the state-of-the-art methods.
Fresh Concrete Image Data Set Development Using Data Augmentation Algorithm as Building Concrete Compression Identification Reference

Image is one of the most important multimedia data that is widely used in information technology. Information can be represented by image and human eye can analyze and interpret the information in accordance with the objective. In the field of civil engineering, image processing is used to help identify and classify concrete quality classes based on a collection of concrete image data as in this study, namely classifying the concrete compressive strength in 3 qualities fc 22.83 Mpa, fc 24.90 Mpa and fc 26.98 Mpa. The purpose of this study is to develop a concrete image data set with data augmentation because in image processing, an adequate data set is needed so that machine learning can do learning on training data and model the data set perfectly. The method used in developing this data set uses augmentation data, which is a series of techniques or methods that increase the size and higher quality of the training data so that algorithm modeling using Neural Networks can be built better than 45 concrete images with 3 concrete quality classes. The types of data augmentation used are color augmentation with Contrast and position augmentation with re-scale, rotate, translation and flip. The results of each augmentation process produce 90 images of each concrete quality so that the total data set obtained is 270 concrete images. This data set will be used to build a compressive strength of concrete identification algorithm using GLRLM and obtain a compressive strength of concrete classification algorithm using a Neural Network.
Enhancement of SPECT reconstructions by means of coregistered MR data

The diagnostic potential of brain SPECT imaging is limited by its low spatial resolution. Here an iterative reconstruction technique is proposed which is based on the Conjugate Gradient (CG) algorithm and offers the possibility of improving the spatial resolution of the SPECT activity map of a patient, provided that an MR scan of the same patient is available. The SPECT data are reconstructed twice. The first reconstruction is to permit an accurate coregistration of the MR image to the SPECT image. Then, the coregistered MR image is used as the starting point for the CG algorithm, which solves a "shifted problem" As the iterations proceed, the MR signal of low spatial frequency is replaced by the SPECT signal. However, the high spatial frequency anatomical details contained in the MR image are slowly destroyed by the SPECT projector-backprojector operator since they are nearly outside its range. They remain in the iterates and determine their spatial accuracy. The number of performed iterations and another parameter tune the MR bias on the SPECT reconstruction.
Multi-scale/attention Convolutional Network for Diffuse Large B Lymphoma Segmentation in PET Image

Diffuse large B lymphoma (DLBCL) is one of the most frequent lymphomas, and Positron emission tomography (PET) is an important imaging tool for tumor diagnosis. It can be used to obtain information such as tumor size, location, etc. Clinically, manual segmentation of lesions is time-consuming and labor-intensive. Therefore, in this paper we propose an automatic segmentation model called CBS-APU-Net based on U-Net framework. This model fuses multiple scale and attention mechanisms. During downsampling phase, the CBS module utilizes multi-scale convolution to extract high-dimensional features, and the local receptive field is enlarged using the atrous spatial pyramid pooling at the bottom of the model. The attention mechanism is introduced in the skip connection part so as to enhance the network attention of the lesion area in the feature map. While in the upsampling part, the features after high-dimensional deconvolution are fused with low-dimensional features. Experimental results show that this model achieves the best performance after 500 iterations, and the overall Dice coefficient might reach 0.8189, which outperforms than current models for DLBCL automatic segmentation.
Simulation And Real Time Of VR Controlled Robotic Manipulator Using ROS

This paper focuses on developing a virtually controlled robot by integrating Virtual Reality (VR) and Robot Operating System (ROS). Robots can be used in certain environments where humans cannot be physically present to undertake a task. To increase the safety of humans, controlling a robot virtually is one of the best solutions. This paper shows how the VR-controlled manipulator will help in different fields like hazardous environments, underwater research, and the medical field for surgical operations. Unity 3D is used to develop the virtual environment, and ROS is used for communication with the physical robot. A Virtual environment would be developed where an exact robot (URDF) model can be designed. The interaction with the virtual environment is done with the help of VR headsets and controllers. ROS acts as the communication bridge between virtual and physical robots. A prototype has been developed to be controlled through virtual controllers that can interact through ROS.
A Sliding Mode Control Scheme for Quadrotor Based on Fixed-time Extended State Observer

A tracking trajectory problem for a quadrotor via second order sliding mode is concerned. Traditional sliding mode controller exists chattering phenomenon among control input when the state variables stayed in sliding surfaced. This phenomenon that is caused by sign function which is used for anti-disturbing might stimulate the unmodeled dynamics. In past articles and essays, using deceased coefficients of sign or replaced that sign function with saturation function are to restrain chattering phenomenon. In this paper, considering using sign function to suppress internal unmatched coefficients and external disturbances during quadrotor operating, by employing sliding mode combined with fixed-time observers to handle the chattering phenomenon. The significance of utilizing fixed-time observers in control scheme is to observe state variables and disturbances of system in fixed-time. Thereby, the sign function is substituted by observation value, which means anti-disturbing term changed from sign function to observation value. Sequentially, under the control scheme the chattering phenomenon is minished or even eliminated. Finally, a comparative simulation demonstrates that the presented scheme can improve control performance and restrict chattering phenomenon.
A Novel PSO Approach for Cooperative Task Assignment of Multi-UAV Attacking Moving Targets

Collaborative task assignment is the fundamental research of multi-UAV coordinated control problem. The key factors affecting the task assignment are analyzed and a comprehensive evaluation system is established considering the range, time window and attack reward. In the light of it, an optimal particle swarm (PSO) task assignment algorithm is proposed based on local random search (LRS) and variable neighborhood search (VNS), and a particle swarm codec algorithm is designed to solve the "deadlock" problem caused by simultaneous attacks. Meanwhile, the adaptive learning factors are introduced during the process of particles updating. To improve the global search capability of the algorithm, the LRS algorithm is embedded in the framework of VNS-PSO. Finally, some simulation experiments are proposed to verify the effectiveness of optimized PSO. Compared with VNS-PSO, LRS-VNS-PSO has better global searching ability to solve the task allocation problem in the scenario of simultaneous attack of multiple UAVs.
Perspectives of Software Engineering Class as Gen Z Learners on the Sustainability of Online Learning in New Normal Towards Metaversity Students

In New Normal, after almost two year learning from home or anywhere, university students are back to learn onsite in conventional classrooms. However, IT students at an Institute in a remote area have complained of learning diversion as the downside of this method. This case study designed qualitatively aims to explore the perspectives of 64 applied software engineering students, whose ages and learning characteristics are categorized as Gen Z (Millenium) sampled conveniently as subjects/informants in this study, on the sustainability of online learning post pandemic and how the mode could be adjusted to stimulate students' motivation in New Normal. Units of analysis consisted of the students' Toulmin's model based-argumentative essays on the learning approach best suited their needs post pandemic analyzed by using thematic-content analysis. The results were validated by in-depth interviews with 5 students out of the 64 informants and 21 their senior from the same field. This 8-month study revealed a significant number of students supported the sustainability of online learning in New Normal due to its time and space flexibility, inevitably leading to boundary free learning as this approach was experienced to allow them to self-regulate their learning and enjoy learning independency. All interviews verifying the main finding shows students' desireto be still offered the option for sitting online class post pandemics also indicated their technological self-efficacy and satisfaction further boosting self-confidence for transitioning towards Metaversity learners. The implication of this study is an insight into the learning mode actually working the best for applied software engineering learners in New Normal along with a recommendation that online learning should be persisted and indeed, need its delivery integrating withMetaverse applications to better encourage this Gen Z in their academic study, for they tend to be more confident when learning virtually rather than traditionally.
Reinforcement Learning-based Motion Generation for a Tracked Robot to Go Over a Sphere-shaped Non-fixed Obstacle

Tracked robots have high traversability over rough terrain. However, even for such robots, it is still challenging to traverse terrain with non-fixed obstacles which may move when the robots go over them. Therefore, we propose a reinforcement learning-based method to generate the motion of the tracked robot to go over the obstacle. We set a task where the robot attempts to go over a sphere-shaped non-fixed obstacle and reach the goal. To succeed in the task, we designed a reward function so that the robot can reach the goal as straight as possible. As a training algorithm, Deep Q-Network was used and the robot was trained in a dynamics simulator. It was confirmed that the robot succeeded in the task using the trained network, which generated motion for going over a sphere-shaped non-fixed obstacle.
Robust Stabilization for Fuzzy Systems via Event-Triggered Integral Sliding Mode Control Method

An event-triggered integral sliding mode control method is presented to investigate the stabilization problem for a class of single-input T-S fuzzy systems. Different from the related results, two kinds of sliding mode switching functions are proposed to analyze the reachability of the sliding surface and design the controller. In order to reduce the burden of information transmission, the triggering state is used to construct the integral sliding mode switching function. Using the triggering-state-dependent switching function, the event-triggered integral sliding mode control law is designed, which can guarantee that the system trajectories can reach the sliding surface. Furthermore, a time-varying triggering threshold is used in the event-triggered condition to guarantee that the system trajectories can exponentially converge to a bounded region. Moreover, a sufficient condition of ultimate boundedness for sliding motion is given to solve the controller gain. Meanwhile, the proposed method does not require that the assumption that the inter execution time has a uniform upper bound. Finally, an example is presented to show the effectiveness of obtained theoretical results.
Control of beam halo formation through nonlinear damping and collimation

1098-4402= This paper demonstrates that transverse beam halos can be controlled by combining nonlinear focusing and collimation. The study relies on one-dimensional, constant focusing particle-in-cell (PIC) simulations and a particle-core model. Beams with linear and nonlinear focusing are studied. Calculations with linear focusing confirm previous findings that the extent and density of the halo depend strongly upon the initial mismatch of the beam. Calculations with nonlinear focusing are used to study damping in the beam oscillations caused by the mismatch. Although the nonlinear force damps the beam oscillations, it is accompanied by emittance growth. This process is very rapid and happens within the first 2–3 envelope oscillations. After this, when the halo is collimated using a system of four collimators, further evolution of the beam shows that the halo is not regenerated. The elimination of the beam halo could allow either a smaller physical aperture for the transport system or it could allow a beam of higher current in a system with the same physical aperture. This advantage compensates for the loss of particles due to collimation.
Research on the Relationship between Eddy Current Loss and Seawater Parameters of Underwater Wireless Power Transfer System

Eddy current loss is always an important problem for underwater wireless power transfer (WPT) system, which can cause energy loss and other problems compared with WPT system in air. In this paper, the eddy current loss in the seawater medium is calculated and equivalent to the power consumed by the eddy current resistance in the circuit, so as to modify the circuit model of the underwater WPT system. Finally, the above conclusions are verified by simulation analysis, and the influence of seawater medium parameters on eddy current resistance is studied.
Discriminant method approach for harvesting forest operations

Forest harvesting operations are complex resolution problems where different factors of different nature intervene. These operations are affected by the nature of the trees to be harvested, the environment where they are planted, the operator who performs the operation and the shift in which it is performed, among other aspects. These factors affect the productivity of the harvest, which, in turn, being the first link in the forestry supply chain, affects the rest of the links. Poor management of harvest operations can lead to critical setbacks and delays in the forestry supply chain. In this work, it is proposed to develop productivity prediction models that allow adequately estimating productivity considering the simultaneous impact of all the factors or variables that intervene. For this, the data collected automatically by the harvesters are analyzed using the linear discriminant method. The results allow us to infer that the approach is adequate to generate these models, particularly when the target set to be predicted is partitioned.