Reviewerly/exHarmony huggingface dataset:
A Bit Synchronization Detection Method for High Dynamic and Weak GNSS Signal

In order to achieve bit synchronization in high dynamic and weak signal conditions, this paper presents an integrated method of bit energy detection and single bit-largest energy statistics, bit energy integrated detection method for short. Firstly, do non-coherent accumulation with coherent integrated value in the same period of 20 candidates, each energy value accumed after N bits, and then, find out the index of the maximum accumulated energy in 20 routes, denoted P. At the same time, calculate the times of energy in N bits of 20 route candidates and find out the index of max times in 20 routes, denoted K. If K equal P, bit synchronization succeed, otherwise lost. Simulation results show that, probability of success does not improve comparing bit energy integrated detection method with bit energy detection method, but it reduced the probability of error into the data decoding links, ensured the accuracy of the data demodulation, and improved the reliability and accuracy of the subsequent algorithms.
Anchor-Free and Pixel-Wise Convolution for Visual Tracking

Most of the existing trackers usually rely on a multi-scale searching schemes or predefined anchor boxes to accurately estimate the scale and aspect ratio of objects. However, this operation requires tedious and heuristic configurations to obtain ideal results, which is inefficiency. To solve this problem, we introduce an anchor-free and pixel-wise convolution algorithm (named SiamAPF). SiamAPF transforms the tracking problem into a classification problem and a regression problem, and directly performs classification and bounding box regression on the target in fully convolutional network. The proposed network architecture is simple, and does not need to set responsible hyper-parameters. Meanwhile, it can improve the network's ability to express spatial information through pixel-wise convolution. It achieves state-of-the-art performance on the test datasets OTB100, UAV123 and runs at 50 FPS. In order to achieve better engineering applications, we successfully deployed SiamAPF under the NVIDIA Jetson Xavier hardware platform and recorded video for verification.
Application of Improved Ant Colony Algorithm in Path Planning of Underwater Robot

With the continuous deepening of scientific development and technological innovation, many algorithms have been derived from the research on robot path planning, including the current research on ant colony algorithm. It is often accompanied by a series of problems such as slow convergence speed, seeking optimal solutions according to local conditions, insufficient optimization ability, and inability to complement the diversity of ant colony species and convergence speed. In view of the current shortcomings, first, the initial pheromone distribution is uneven, which can avoid the useless search behavior trajectory of the ants during the movement process, thereby speeding up the solution speed and saving time; secondly, based on the advantages of the A* algorithm, the introduction into its algorithm The heuristic search function further improves the heuristic function of the traditional ant colony algorithm and maximizes the speed of the search. Considering the influence factors of energy consumption factors on the experiment in actual situations, a new heuristic function is formed by combining with traditional factors, a simulation environment is built, and the proposed method is verified and compared through a set of examples. The results show that the improved ant colony algorithm is in the optimal path. It has better performance in length and number of iterations, and has practical engineering significance for robot path planning.
A Top-k Similarity Node Search Method Based on Convolutional Neural Network in Complex Network

In complex network, it is challenging to find similar nodes for a given node. Existing methods focus on evaluating the structural similarity between nodes but ignore their textual similarity, which makes returned nodes are not accurate enough. In order to search top-k nodes with similar text and structure for a given query node in complex network, we propose a similar node search algorithm based on convolutional neural network (LRE-CNN). For a weighted complex network, we first construct the nearest neighbor network model based on degree and weight for each node. On this basis, relative entropy and joint tightness are used to evaluate the comprehensive structural similarity between nodes. We get the candidate nodes with the highest structural similarity to the query node. Next, convolutional neural network is used to extract and compare potential text features between query node and candidate nodes. Finally, top-k similar nodes are returned for the given query node. Through experiments on complex networks of different scales and comparison with existing node similarity measurement methods, the results demonstrate that LRE-CNN has high retrieval accuracy and high execution efficiency. It can be effectively applied to top-k search of similar nodes in large-scale complex network environment.
Optimal Avoidance Path Planning and Guidance Method for Ground Targets Based on Artificial Repulsion Field

This paper mainly completes a research on real-time optimal path planning and guidance method for ground targets to be evaded, proposes an artificial repulsive field modeling method for different area sizes and different importance levels of ground targets, and generates the current optimal evasive flight path and corresponding guidance instructions based on real-time landing point estimation information. In this paper, the repulsion field model for a single area and the repulsion field modeling method for multiple areas are given, as well as the derivation process of the optimal avoidance path guidance instruction.
On Distributed State Estimation with Bearing Measurements

State estimation with multiple sensors is an important topic in guidance and target tracking problems. This paper is concerned with the distributed state estimation over bearings-only sensors where each sensor’s measurements are the bearings from the sensor to a moving target. Firstly, the distance from local sensors to the target is estimated by combining bearing measurements of neighboring sensors. The virtual position measurement is given using the distance estimation. Then, the distributed Kalman filter based on the distance estimation is constructed to estimate the moving target’s states. Finally, numerical simulations results show the effectiveness of the proposed algorithm.
B-Spline Fusion Line of Sight Algorithm for UAV Path Planning

One of the key technologies to make UAVs intelligent is route planning. The traditional A* algorithm has a series of problems when performing UAV path planning, such as the planned path is not the actual optimal route, does not satisfy airworthiness criteria of UAV and so on. In paper, we come up with an A* improvement strategy based on B-sample fusion to improve the line of sight detection algorithm. Firstly, the line of sight detection algorithm is introduced and improved to optimize rationality of route planning at any angle. Second, to lessen the memory overhead and accelerate search speed, an adaptive cost function is used to decrease the amount of expansion points in the process of route planning. Finally, experimental simulation results make it clear that the revised approach suggested in this article is substantially faster than the standard A* algorithm in generating viable pathways in terms of planning efficiency and immediacy.
Kinematics Modeling and Analysis for Mobile Manipulator

Mobile manipulators integrate with the mobility of mobile platforms and the manipulation of the multi-degree of freedom manipulators, and have the superiority as large workspace, flexibility and intelligence, etc. According to the configuration of the mobile manipulator, the differential-drive mobile platform movement is converted to virtual joints, i.e., prismatic and revolute joints, together with the multi-degree of freedom manipulator, the mobile manipulator can be built as 2P7R joint configuration, which realizes the whole forward kinematics modeling of mobile platform and multi-joint manipulator, and thus overcoming the traditional divide and conquer modeling method. The analytical modeling of the whole forward kinematics for the mobile manipulator is verified through the Robotics Toolbox. Besides, the workspace of mobile manipulator is analyzed as well, which provides an important reference for high dimensional motion planning and motion control of the mobile manipulators.
Vision Transformer with Information Bottleneck for Fine-Grained Visual Classification

Fine-grained visual classification focus on accurately identifying the subordinate categories from a base class. One key of this task is to find discriminative local parts. Convolutional neural network-based methods using attention mechanism can enhance the representation of local regions and improve the classification accuracy. Recently, vision transformer has shown great potential in traditional image classification with its self-attention mechanism, which can grab important regions. However, one main difficulty in fine-grained visual classification is the subtle inter-class variance. Different classes may share the same or similar features and these features may be identified by attention. This discriminative information is redundant for related classes and may damage the performance of network. To this end, we apply information bottleneck to vision transformer training to relieve the interference of redundant information. The experiments demonstrate the effectiveness of this simple method.
An Image Enhancement Method Based on Adaptive Gamma Transformation

Aiming at the problem that it is challenging to extract visual features from images under different light conditions effectively, a kind of image enhancement algorithm based on adaptive gamma transformation is proposed in this paper. First, the image is transformed from RGB space to HSV space to obtain the feature map of brightness. According to the brightness of the image, a kind of adaptive gamma transformation function is constructed to adjust the brightness of the image. And then, the CLAHE algorithm is used to enhance the brightness of the image and increase the contrast of the image further. The results show that the algorithm effectively improves the chroma and entropy of the image under different luminance conditions while improving the adaptability to the lighting luminance effectively.
Multimodal Control of UAV Based on Gesture, Eye Movement and Voice Interaction

Unmanned aerial vehicles (UAVs) have been widely used in various fields of life and production. However, a great number of UAVs are controlled by the remote controller, which requires a long learning and adaptation time and performs a low interaction efficiency. The multimodal control of UAV can overcome these shortcomings. A multimodal control system integrates multiple natural interaction modes, such as gesture, eye movement and voice interaction. The learning and adapting time of multimodal control is short, the multi-channel joint interaction efficiency is high, and people's hands can be liberated. This paper proposes a multimodal control method of UAV based on eye movement, voice and gesture interaction. These three interaction modes cooperate with each other to realize the flight control of UAV. The proposed method can maintain the controllability of UAV in dark or noisy environment. Experiments show the effectiveness of the proposed method.
Multi-UAV Task Assignment Based on the Improved Discrete Pigeon-Inspired Optimization Algorithm

Aiming at the multiple Unmanned Aerial Vehicle (multi-UAV) task assignment problems, a multi-UAV task assignment algorithm based on the improved discrete pigeon-inspired optimization (PIO) algorithm is proposed considering various fitness functions and constraints. And a correction algorithm is designed for the constraint overflow problem in the algorithm. First, a multi-UAV task fitness function problem model is established with various benefits, costs, and constraints. In addition, referring to the idea of the learning factor in the particle swarm optimization (PSO) algorithm, the PIO algorithm is improved to strengthen the learning ability of the pigeons for global and local optimal information. Then, the improved PIO algorithm is discretized to fit the discrete task assignment model. Finally, aiming at the constraint overflow problem, a constraint check correction algorithm is designed to correct the constraint overflow sequence. Simulation experiments show that the improved discrete PIO algorithm can effectively solve the multi-UAV task assignment problem.
On Comprehensive Evaluation of Excellent College Teachers from the Perspective of Fuzzy Mathematics

Excellent teacher in the process of education play a professional role model and lead exemplary role, which determines the excellent teachers the importance of the selection criteria and evaluation method, is an important part of the teacher training system. However,due to the fuzziness of the selection criteria for excellent college teachers, it is difficult to accurately reflect this evaluation through specific scores. The comprehensive evaluation of excellent college teachers based on fuzzy mathematics can meet this demand. The comprehensive evaluation of excellent college teachers based on fuzzy mathematics firstly constructs the comprehensive evaluation model of fuzzy mathematics, transforms the expert matrix to get the feature vector by product root method, then carries on the consistency test, and then determines the index weight of the evaluation standard. On the basis of determining the weight index system, zadeh calculation is carried out by fuzzy matrix and evaluation matrix, and finally the comprehensive evaluation results of excellent college teachers can be obtained.
Generation of Lateral Navigation Transition Path Based on Dubins Method

The lateral navigation transition plays an important role in the Flight Management System (FMS) to complete the flight mission. Especially, if there is a large deviation between the aircraft and the defined leg, FMS enables the aircraft to acquire the defined leg of the flight plan by lateral navigation transition. The generation of constructing lateral navigation transition paths based on Dubins method is proposed in this paper. At first, lateral navigation transition paths were classified according to the current position and bearing of the aircraft. Then the positions and bearings of each segment of lateral navigation transition paths were calculated when the defined leg information is known. Finally, the instructions generation algorithm was designed based on each segment of transition paths, and simulations of lateral navigation transition were carried out to verify the validity of the proposed methods. The results of simulations demonstrated that the methods of the lateral navigation transition based on Dubins method were available to FMS.
Adaptive Fuzzy Nonsingular Finite-Time Control for Uncertain Nonlinear Systems

In this article, an adaptive fuzzy nonsingular finite-time controller (AFNFC) is presented for uncertain nonlinear systems (UNS). The system uncertainty is addressed by borrowing the fuzzy systems. The backstepping technique is borrowed to design the finite-time controller. For the published finite-time methods, the control singularity may exist since the constructed finite-time controllers contain the fractional power of error. To overcome this problem, we design a differentiable power function in the AFNFC design process. It is shown that the developed AFNFC method can guarantee the finite-time performance.
Software Component Design and Application for Telemetry Data Processing of Launch Vehicle

In order to meet the demand of telemetry data processing of various types of carrier rockets in China, a software component for telemetry data processing of carrier rockets based on TT&C software component technology is proposed. Aimed at the functional requirements of dynamic calculation of mathematical formulas in the operation of launch vehicle telemetry data processing software, the mathematical operation construction is designed, which improves the timeliness of data processing. It has been proved by practice that the componentization of TT&C software can provide theoretical guidance for specific engineering practice and strong support for the efficient completion of software engineering research and development.
Research on Trajectory Prediction of Near Space Target Based on NAR Neural Network

High-precision trajectory prediction for gliding targets in the near space is the key to accurately calculate and predict interception points and successfully conduct anti-proximity operations. In order to realize the trajectory prediction of near space target, a nonlinear autoregression (NAR) neural network is introduced to study the trajectory prediction. First, on the premise of estimating the target acceleration through state tracking, the acceleration variation law of the target in the future is predicted by the NAR neural network; Then in each forecast period, the flight trajectory of the target is predicted by extrapolation with constant acceleration model. The simulation results show that the proposed trajectory prediction algorithm can effectively predict the flight trajectory of the target in the near space, and its accuracy can meet the needs of interception operations.
Design of Longitudinal Automatic Carrier Landing System Based on Reinforcement Learning

This paper mainly aims at automatic carrier landing task under high sea conditions, and proposes a method for designing the landing guidance law based on reinforcement learning, and the effect of which is also tested and analyzed. This paper first established the longitudinal small perturbation equation, control law model, ship surface atmospheric environment model and aircraft carrier deck motion model of F-18 carrier-based aircraft as the guidance law training environment. For better docking engineering practice, the outer loop of the automatic carrier landing system (ACLS) designed in this paper adopts landing guidance law based on reinforcement learning, and the inner loop adopts traditional $$\dot{H}$$ tracking control law. It is found that the landing accuracy of the guidance law based on reinforcement learning is significantly better than that based on the PID algorithm. When facing steady-state disturbance, it can anticipate disturbances and resist them to ensure the accuracy of the mean value of landing point. In the face of random disturbance, it can also generate more appropriate instructions and reduce the distribution range of landing points. In this paper, the Markov decision process modeling method of ship landing mission is also studied from the perspective of flight dynamic. The result shows that it is very important to select the appropriate state quantity as the input. And after adding random factors into the state transition function, the training effect of guidance law is significantly improved. The random environment should consider not only random distribution, but also amplitude. Adding random amplitude disturbance can significantly improve the training effect.
Design of Formation Holding Control for UAVs Maneuver Flight in Formation Based on Virtual Structure

As a control strategy of the formation, the virtual structure method assumes that there is a structure existing in space to describe the desired configuration of the formation, and defines a set of reference points corresponding to the expected points for each UAV in the formation one by one. When all UAVs in the formation can stably track the motion of their expected reference points, the desired formation motion is realized. Firstly, the single UAV model is established, and combining with the error model of relative plane motion in the formation coordinate system, we can build the motion model of the whole formation system. In addition, according to Lyapunov direct method, the stable and independent virtual point trajectory tracker and formation holder are designed, while differential and integral method are used to improve the control efficiency of formation holder. Finally, under the condition of meeting the performance constraints of UAVs, an ideal formation maintenance during formation maneuver flight can be realized.
Distributed Feature Reduction Signal Sorting Algorithm

With the development of science and technology, modern war has changed from traditional mechanized war to electronic war. Electronic warfare has become the mainstream combat mode in modern war. At present, the electromagnetic environment is very chaotic and complex, the pulse density increases rapidly, and the pulse modulation mode is complex and changeable. In order to deal with the low efficiency of traditional methods in complex electromagnetic environment, a distributed feature reduction signal sorting method is proposed. Firstly, attribute entropy regularization is added to the objective function, and different weights are given to different features. Secondly, the feature reduction process is added to select which dimension of features to be discarded by setting the threshold. Each iteration of the algorithm will update the membership matrix, cluster center and feature weight matrix until the termination conditions is satisfied. Simulation results show that the algorithm can reduce the number of iterations and time, and improve the accuracy of radar signal sorting results by reducing the number of features.
Hybrid RRT*-Gauss Pseudospectral Trajectory Planning Method for eVTOL Application

The complexity of trajectory design for electric vertical takeoff and landing aircraft (eVTOL) originates the nonlinear aircraft dynamics, path constraints, and coupling characteristics between translational and rotational motions, which in turn slows down the solving process. To this end, this paper proposes a hybrid trajectory planning method composed of two steps. Firstly, the Rapidly-exploring Random Tree* (RRT*) planner is used to generate a feasible flight route in the presence of obstacles. Thereafter, the cube B-spline curve and differential flatness are employed to generate a smooth trajectory based on the waypoints from RRT*, which serves as the initial guess of Gauss pseudo-spectral method. As such, the advantages of global and numerical trajectory methods are combined, which simultaneously ensures that the route conforms to the aircraft dynamics and control input conditions, and the optimality of the trajectories. A test case is presented to demonstrate the effectiveness of the approach for designing trajectory of a multi-rotor eVTOL, which shows the hybrid RRT*-Gauss pseudo-spectral algorithm performs well in computation time, maximum relative error and control resource consumption.
Trajectory Tracking of Maneuvering Reentry Vehicle Using Nonlinear Estimate Model

Due to the uncertain maneuverability of the maneuvering reentry vehicle (MaRV), the state estimation of such targets has been a research hotspot. In order to improve accuracy of trajectory tracking using traditional kinematics and dynamics estimation models, a target estimation method based on nonlinear maneuvering coefficient model is proposed. Firstly, on the basis of studying the characteristics of the reentry maneuvering vehicle’s jumping and reentry stage, the state estimation model including the vehicle’s maneuvering coefficient is analyzed. Then, in order to improve the accuracy of Kalman filtering method for aerodynamic parameters, the deviation model of the maneuvering coefficient described by the first-order Markov process is established. The aerodynamic data with the error is introduced as a priori information into the state equation. The maneuvering coefficient is estimated simultaneously. Finally, a mathematical simulation study was conducted. Simulation results show that this method can improve the estimation accuracy for this kind of target to some extent.