Reviewerly/exHarmony huggingface dataset:
An Interaction Control Framework for Regular Aerial Manipulators in Push-and-slide Operations on Moving Objects

Aerial manipulators (AMs) have evolved to fulfill various interaction tasks, such as push-and-slide tasks. However, a push-and-slide operation on a moving object is still challenging for a regular AM because its flying platform is usually a rotorcraft unmanned aerial vehicle (UAV)-a floating base with complex aerodynamics, under-actuation property, body vibration, etc. This paper proposes an easy-to-implement approach-an interaction control framework based on a remodeling idea of a closed-loop system, allowing an AM to steadily transition from a free flight to a contact flight and slide along a moving object. Then, a decoupled control idea is applied to implement this framework on an AM system. Besides, a contact model and a trajectory planning issue in application scenarios are also studied. Finally, several push-and-slide operation experiments evaluate the proposed interaction control framework. The experimental process and results validate its effectiveness.
Task Allocation and Trajectory Planning for Collaborative Adversarial Multi-UAVsystems

A algorithm-based approach for cooperative task allocation in multi-UAV adversarial missions is proposed. Taking into account flight time., fuel consumption., and introducing adversarial benefits as the optimization objective., the task allocation optimization mathematical model is constructed., considering flight constraints of the UAVs. Subsequently., a CLPSO algorithm is proposed for solving the problem., which exhibits superior global search capability compared to existing PSO algorithms. First., the particle positions are encoded as a set of matrix vectors., from which a corresponding set of feasible task assignment solutions is decoded., achieving the discretization of the particle swarm algorithm solution. Next., the CLPSO is employed to calculate the individual best and global best solutions for the particles., determining the corresponding strike sequence. Finally., a B-spline curve is fitted to the obtained strike sequence to generate a smooth path that is suitable for UAV flight. Simulation experiments demonstrate that the proposed method effectively addresses the task allocation problem in heterogeneous multi-UAV collaborative adversarial scenarios.
Dual Closed-Loop Trajectory Tracking Control of Air-Ground Amphibious Unmanned Platform in Off-Road Terrain

Air-ground amphibious robots are becoming the focus of research in the field of robotics, and trajectory tracking control has always been a research hotspot in the field of robotics. Due to the fact that air-ground amphibious unmanned platform often operates in off-road terrain where the roads are non-structured, with high unevenness and strong external interference, it is necessary to study trajectory tracking issues in off-road terrain. This paper proposes a dual closed-loop trajectory tracking controller based on the kinematic model of a tracked robot for addressing the insufficient steering issue during trajectory tracking in off-road terrain for air-ground amphibious unmanned platforms. Based on the preview information, the outer loop uses the model predictive control algorithm to solve the ideal value of combined speed and differential speed of tracks on both sides. For the problem of insufficient steering of the platform in a complex environment, the PID method is used to design the feedback control rate to compensate for insufficient steering. The dual closed-loop controller ensures the optimal control of the platform. Finally, the effectiveness of the dual closed-loop control structure is verified by Matlab / Simulink simulation.
Efficient CNN-Based Automatic Modulation Classification in UWA Communication Systems Using Constellation Diagrams and Gabor Filtering

Underwater Acoustic (UWA) communication channels are varying in nature due to various underwater conditions. The efficiency of the UWA communication system can be increased by changing transmission parameters over UWA channels based on the state of the channel. The aim of Automatic Modulation Classification (AMC) is to recognize efficiently the modulation schemes from signals received over UWA channels. In this paper, we propose an efficient method that combines equalization, Gabor filtering of constellation diagrams, thresholding and deep convolutional neural networks (CNNs) for modulation classification over the Signal-to-Noise Ratio (SNR) range of -10 to 30 dB in UWA systems. Simulation results of the proposed method indicated an acceptable performance over a wide SNR range.
Road Target Detection Algorithm Based on Improved YOLOv5

Aiming at the problem that the existing road target detection algorithm is difficult to deploy lightweight., this paper proposes an improved YOLOv5 algorithm for road target detection based on the YOLOv5 network framework. By reconstructing the network structure of the Backbone and Neck parts, the number of model parameters and calculations are reduced to make the model lightweight, by introducing Stem Block and CBAM, the model structure is optimized to improve the model detection accuracy, re-clustering Anchors to improve the target positioning ability, The comparison and ablation experiments were carried out using the parameter amount, calculation amount, AP, mAP, and FPS as evaluation indicators. While meeting the requirements of real-time detection, the algorithm in this paper has good target detection accuracy, and the parameter amount and calculation amount are reduced by 29.0% and 36.2% respectively compared with the original model, which is suitable for road target detection tasks.
Probabilistic Prediction of PV Ramp Events Based on LSTM-Bootstrap

In recent years, renewable energy has received widespread attention. However, due to the impact of climate change, the output power of photovoltaic (PV) power generation is highly variable, which poses a challenge to grid scheduling. The PV power ram event, i.e. the drastic change of PV power in a short period of time, has a particularly obvious impact on the power grid. This paper first summarises the current state of PV ramp-up research. Then, three common definitions of PV ramp-up events and the definition chosen in this study are described in detail. Then, a new ramp prediction method is proposed: first, the mRMR feature selection method is used to screen the input features; then, the LSTM network is constructed to predict the PV power; finally, the prediction intervals with different confidence levels are given in combination with the bootstrap method, and the probability of the ramp is determined accordingly. The effectiveness of the method is verified by simulation. This study provides new ideas for further improving the accuracy of PV ramp prediction. The experimental results show that by selecting the appropriate ramp threshold and determining the ramp probability, this method has achieved better results in terms of accuracy, hit rate, critical success index and other evaluation indicators.
Time-scale-based Analysis of Off-site and On-site Albedo of A Photovoltaic Power Plant

With the vigorous development of clean energy, photovoltaic (PV) power generation accounts for an increasing proportion of total power generation. The efficiency of photovoltaic power generation is closely related to the albedo of the photovoltaic power plant. In order to improve the efficiency of power generation, it is necessary to deeply analyze the ratio of irradiance received by PV panels and their losses. The data in this paper come from the albedo of a PV power station in Gansu Province, and the analysis shows that the albedo of the station is always higher than the albedo of the station due to the different vegetation cover on the surface of the station, and then the trend of the albedo over time is analyzed in different time scales, and it can be found that the albedo is lower when the irradiance is higher, for example, the noon of a day and the summer of a year; and the albedo is higher when the irradiance is lower, for example, the early morning of a day and the winter of a year. early morning of a day and winter of a year.
A Vision-Based Ground Moving Target Tracking System for Quadrotor UAVs

Nowadays, unmanned aerial vehicles (UAVs) are playing their potential and role in various fields due to their unique advantages, and many tasks require UAVs to have the ability to stably track ground targets. In this paper, we design a vision-based tracking system, using which a quadrotor UAV equipped with a fixed monocular camera can track a moving target on the ground. The visual scheme includes a lightweight YOLOv3-tiny detector and a KCF tracker to provide ground target perception. And the relative position estimation between UAV and target is obtained according to the line-of-sight angle calculation and the Kalman Filter method. As for the real-time approach of the quad rotor UAV to the target, we adopt a PID velocity controller to provide continuous tracking capability. Finally, the results of simulation and flight experiments demonstrate that the designed system can enable the quadrotor UAV to perform stable tracking of a moving target on the ground.
An Enhanced Deep Super-Resolution Generative Adversarial Network Approach for Skin Disease Image Regeneration and Assessment

Skin cancer is a critical medical concern, posing significant challenges in accurate diagnosis. Algorithmic approaches have seen remarkable advancements across various occupations, including skin disease assessment. This study introduces a distinctive Improved Deep Super-Resolution Generative Adversarial Network (I_DSUR-GAN) methodology for regenerating low-resolution (LOR) skin disease images into super-resolution (SUR) format. Additionally, a modified SUR-dataset inspired by HAM10000 is presented for skin disease applications. The proposed approach incorporates a novel loss function design to provide supplementary information, facilitating the creation of high-quality SUR images. Experimental results demonstrate that our method outperforms existing approaches on the HAM10000 dataset. A comprehensive evaluation and employing sensitive metrics is conducted to assess the effectiveness, training periods, and memory requirements of the proposed framework. The outcomes reveal that the suggested model excels in restoring and identifying hue and texture compared to conventional and earlier models.
Intention Recognition Trustworthiness Measurement using a Spatiotemporal Knowledge Graph

The generalization ability of current intent recognition methods is insufficient, and the evaluation of confidence level of intent recognition is the key to its popularization and application in the high-reliability military and civilian fields. The spatiotemporal Knowledge Graph can uniformly represent and efficiently organize entities containing spatial location information and temporal information. Battlefield situational objectives and their relationships can be abstractly expressed as a triplet composed of two specific types of spatiotemporal entities and combat intent relationships. Using typical confrontation scenario data to train neural networks, and fusing the confidence levels of the entity and Knowledge Graph, the confidence level evaluation results of the intent triple facing the existing spatiotemporal knowledge base are calculated. The simulation test results show that the method can effectively evaluate the confidence level of intent recognition results, and the accuracy rate is improved by more than 9% compared with the KGTTM model. This method breaks the “closed” assumption of the Knowledge Graph update and completion algorithm from the level of algorithm thinking, and completes the last link of the popularization and application of intent recognition methods.
Body-OGM Based Grid Mapping Algorithm for Obstacle Avoidance of UAVs

The real-time establishment of obstacle avoidance mapping and path planning determines the smoothness of flight trajectories. Currently, grid mapping entails significant resource and time. However, only perception data within the UAV's vicinity is utilized in obstacle avoidance path planning, grid mapping calculation process involves a significant amount of redundancy. In order to address the issues of lengthy establishment of UAV obstacle avoidance maps, based on the body scope, a new dynamic strategy for creating an Occupancy Grid Map is proposed. Pre-processing lidar point clouds and, determining grids occupancy states through ray-cast and Hit-Miss are both applied to reassign the mapping task and optimize construction cost in multi-core environment. Experimental results demonstrate that the proposed method reduces construction time of occupied grid maps, compared with the original algorithm, and satisfies the requirement of trajectory planning in a dynamic environment.
Three-Dimensional Path Planning for Unmanned Underwater Vehicles Based on Age-improved Particle Swarm Optimization

In response to the problems of poor planning accuracy and susceptibility to local optima in traditional swarm intelligence algorithms for path planning of multiple underwater unmanned vehicles (UUVs) in complex environments, we propose an age-improved particle swarm algorithm (AIPSO), which is designed to address the path planning problem of multiple UUVs in complex obstacle environments. Firstly, we use sine chaotic mapping for the initialization process to enhance the dispersion of the initialized particles. Additionally, we introduce a Gauss-Cauchy mutation strategy to promote population diversity and expand the search area. Furthermore, an age factor is incorporated to mitigate the local optima problem by suppressing aged particles, thereby improving planning efficiency and reducing the risk of local optima. Finally, we apply the AIPSO algorithm to UUV path planning. Simulation results demonstrate that the AIPSO outperforms many common algorithms in terms of planning accuracy and planning time for path planning. It is capable of generating safe and smooth paths at a faster speed while satisfying constraints and minimizing costs. These results validate the effectiveness and feasibility of the proposed method in planning trajectories for multiple UUVs in a 3D environment.
Autonomous Perching on Cylindrical Objects for Wild Aerial Vehicle System

Drones are now used in various fields to perform tasks, but there are still many issues with their range and interaction with external objects. In the face of certain situations where drones do not need to hover for a long time, this article proposes a drone autonomous habitat system, which achieves the drone habitat function by designing a gripper, and meets the power performance of the drone with a certain load capacity based on the clamping claw design. A drone control scheme and habitat control strategy have been developed to complete the overall autonomous task. Finally, the autonomous function of the drone was verified through experiments. This article develops a cylindrical item habitat capability for the drone and enhances its maneuverability by designing a bistable habitat device and high-performance quadcopter drone performance and control algorithms.
Intelligent Threat Assessment Algorithm Based on Pre-Wavelet Transform

The current warfare situation is increasingly informationized, intelligent, unmanned, and multi-domain. Therefore, it is of great significance to assist the commander in decision-making and realize the automation of the command-and-control system by performing a fast, accurate and reasonable threat assessment based on the relevant situation changes in the rapidly changing battlefield. Many traditional threat assessment algorithms cannot meet the increasing demand in terms of real-time and accuracy. Based on this, a deep learning algorithm model with a pre-wavelet transform module is proposed. The algorithm model can quickly make threat judgments and the judgment results are relatively accurate, and at the same time, it can continuously optimize calculations in the face of ever-changing and complex situations. Then the possible reasons for the good effect of the front-end wavelet transform module are analyzed, and the similarity between wavelet transform and batch normalization is discussed.
An Ability-Centered Clustering Algorithm for Solving the Construction Problem of Multi-Group Unmanned Platforms

With the rapid development of artificial intelligence technology, unmanned aerial vehicles and unmanned ground vehicles have become increasingly popular as substitutes for humans to perform various search reconnaissance and other tasks. However one unmanned aerial vehicle or one unmanned ground vehicle is difficult to meet the multi-dimensional requirements of the task due to its limited load. This paper conducts research on the construction problem of multi-group unmanned platforms (CPMGUP) based on the requirements of tasks. Firstly, tasks are grouped using the k-means clustering algorithm. Then, an ability-centered clustering algorithm (ACCA) was proposed to solve the CPMGUP based on the requirements of the task groups. Finally, numerical experiments were conducted to verify the effectiveness of the ACCA and compare it with the improved genetic algorithm.
A Multi-Objective Deep Reinforcement Learning Method for Path Planning in Shovel Loading Scenario

The utilization of unmanned mining trucks is gradually expanding. However, planning a path from the starting position to the destination for unmanned mining trucks consumes a significant amount of time due to the irregular environment and the vast search space. On the other hand, deep reinforcement learning holds the potential to expedite the speed of path planning because prior knowledge can be utilized more efficiently. To solve the problem that the network is difficult to converge and easy to get trapped in local optima in shovel loading scenarios and path planning is time-consuming, this paper proposes a method for path planning based on deep reinforcement learning with the residual network and design a reward that considers multiple objectives. This method possesses the advantage that the consumption of time in path planning is reduced and the learning process is easy to converge. Finally, the proposed method is verified in a emulated model using real shovel loading scenario and the result suggests that our method overperforms Hybrid A* in terms of time consumption when the length of paths is similar.
MSGPNet: Modality Synergy and Global Perception Network for Visible-Infrared Person Re-Identification

Visible-Infrared Re-Identification (VI-ReID) aims to retrieve specific pedestrian images from two distinct modalities images. This task presents significant challenges due to the substantial differences between the visible and infrared images. To address this issue, several methods have been proposed to construct intermediate modalities by gray-scale image conversion, GAN-based, or other approaches. However, GAN-based generation methods may introduce additional noise, and constructing intermediate modalities through only gray-scale images cannot take full advantage of the common features in the two modalities images. This paper proposes a method based on generating intermediate modalities based on the paired visible and infrared images, which complement these two modalities. We introduce attention mechanisms to build a Global Perception Module that guides the model to pay more attention to global and channel information, which improves the network's performance. After extracting the features of the two modalities and their complement modalities, we also use modality synergy learning method that leveraging intermediate modalities to reduce the gap between the visible and infrared modality. This helps the network to learn more common features between the modalities. We validated our proposed method on the RegDB and SYSU-MM01 datasets to demonstrate its effectiveness.
A Nighttime Vehicle Detection Method Based on YOLO v8

With the continuous development of autonomous driving technology, how to accurately and efficiently identify the target in some challenge scenarios, e.g. nighttime, has become an important task. Although, in view of cost, pure visual object detection technology has become popular with growing camera performance and image processing capabilities, the pure visual object detection technology based on cameras still faces enormous challenges in accurately identifying targets in environments with poor visibility, such as dusk, night, or rainy or foggy days. This article introduces the state-of art image processing technology, i.e. YOLOv8, to improve target detection capability in the challenge environment. The vehicle recognition accuracy of YOLOv8 is evaluated in low light environments, such as dusk and night. The results show that's the recognition and detection accuracy of YOLOv8 at night surpasses previous versions of the series, making it an important tool for improving autonomous driving safety in poor lighting environments.
Object Detection Method for Autonomous Vehicles in Adverse Weather

Object detection in adverse weather has become a major constraint to the development of autonomous driving. This manuscript presents an object detection model for autonomous driving in adverse weather, which combines the advantages of sensor fusion and domain incremental learning. Filter concatenation and convolution are used to perform data-level radar and camera sensor fusion. The fused information is successively segmented and stored through domain incremental learning. The proposed method provides high accuracy and robustness object detection, even in adverse weather, to provide a basis for downstream tasks and ensure driving safety.
A Novel Bayesian Kalman Filter Under Non-Gaussian Noise for Underwater Integrated Navigation

Affected by the complex underwater environment, underwater integrated navigation usually has to face the problem of measurement loss and non-Gaussian noise. To solve these problems, two kinds of novel statistical similarity measure-based Bayesian Kalman filter are proposed. In order to handle with the measurement loss, the proposed SSMBKF-I uses maximum a posterior estimation (MAP) to determine whether a measurement loss has occurred or not. The proposed SSMBKF-II introduces the probability of measurement loss into minimum variance estimation (MVE). Meanwhile, the statistical similarity measure method is employed for handling with the issue of non-Gaussian noise. Through utilizing fixed-point iteration approach to optimize the cost function to its maximum value, the suboptimal state estimation can be obtained. The simulation examples are utilized for evaluating the performance of the SSMBKF-I, SSMBKF-II relative to the Kalman filter, Bayesian Kalman filter and statistical similarity measure based Kalman filter.
Multiple UAVs Task Assignment Based on Improved Dung Beetle Optimizer

With the increase of battlefield combat tasks, single Unmanned Aerial Vehicle (UAV) has been unable to meet the needs of complex situation, and multiple UAVs gradually occupy an important position. To solve the task assignment problem of multiple UAVs, an improved Dung Beetle Optimizer (DBO) is proposed in this research. Firstly, Sinusoidal chaotic map is introduced into the initial population of DBO to improve the quality of the initial population and the stability of the algorithm. Then the Sinusoidal chaotic map combined DBO (SDBO) is used to solve the objective function of task assignment. Finally, simulation experiments are conducted to verify that the proposed task assignment method has more stable optimization ability and higher adaptability.