Reviewerly/exHarmony huggingface dataset:
Knowledge Tracking Model with Forgetting, Ability and Experience

The knowledge tracking model can evaluate students' knowledge level and learning status according to their learning data, and is an important technical means in the field of educational evaluation. Generally speaking, each student's learning ability is different, and learning is a process of knowledge accumulation, which will be accompanied by forgetting. These fac-tors will have an important impact on the learning effect of students. In order to accurately track students' knowledge status, a knowledge tracking model based on forgetting, ability and experience (F AEKT) is proposed. The F AEKT model uses deep neural networks to analyze and model students' learning data, fully consider the impact of forgetting on learning effects, and comprehensively consider students' learning ability and learning experience in the prediction stage of the model. The effective-ness of the F AEKT model is proved by experiments on two public datasets and comparisons with other knowledge tracking models.
Socially Aware Hybrid Robot Navigation via Deep Reinforcement Learning

Service robots working in public environments require the capacity to navigate among humans and other obstacles safely and socially compliantly. This paper presents a hybrid navigation approach combining rule-based trajectory generators into deep reinforcement learning for motion planning in populated and cluttered environments. An intention-based action space is proposed in the reinforcement learning framework to achieve a tight coupling of the two methods. By fusing static information and dynamic objects, our network can learn motion patterns adapted to real-world scenarios. The rule-based trajectory generator guarantees the safety and dynamic feasibility of the motion primitives. The robot is trained to understand real-time human-robot interactions through deep reinforcement learning. Experiment results demonstrate that our policy can efficiently perceive human interactions and navigate the robot safely in crowded environments with static obstacles.
Velocity Adaptive Quadrotor Trajectory Optimization Based on Gradient Descent Method

To cater for the high autonomy and flexibility requirements of the UAV (Unmanned Aerial Vehicle) velocity adjustment plays an important role in UAV motion planning. Aggressive flight in complex environments may cause the aircraft to lose control or deviate from the pre-planned trajectory, while overly conservative flight can reduce mission execution efficiency. In this paper, we propose a velocity planning strategy that adaptively adjusting the velocity according to the arc of the trajectory, to improve the flight safety while ensuring the efficiency. Furthermore, a temporary target point strategy is proposed to deal with the local optimum problem caused by gradient optimization, improving the planning success rate. Extensive simulation experiments and benchmark comparison demonstrate the effectiveness of our method.
RGB-T Cross-Modal Image Registration Fusing Global Structure and Local Mutual Information

Cross-modal image registration is a prerequisite for visual information fusion in target recognition and robot navigation. However, cross-modal image registration is challenging due to the significant differences in texture and grayscale of different modes of images. To solve this problem, this paper proposes an RGB and thermal (RGB-T) image registration method that combines global structure with local mutual information, called GSLMI. First, the mutual information (MI) in local optimal regions is utilized to calculate the initial value of transformation matrix. Second, global consistent edge structures are detected to obtain similar contents of RGB and thermal images. Third, feature points are extracted by the curvature scale space (CSS) corner detector based on global edges, and the main orientation of points are calculated by the contour angle orientation method. Finally, feature matching is performed with MI providing initial value guidance to achieve the image registration. The accuracy of the proposed method GSLMI is demonstrated by registration experiments on a self-constructed RGB-T images set. The results show that GSLMI can achieve superior performance over the state-of-the-art methods.
UAV Obstacle Avoidance Algorithm Based on Priori Artificial Potential Field and PID-ADRC Hybrid Control

Aiming at the obstacle avoidance problem of Unmanned Aerial Vehicles (UAV) in practical application, this paper proposes a UAV trajectory obstacle avoidance algorithm based on Priori Artificial Potential Field (P-APF), while using PID and Active Disturbance Rejection Control (ADRC) for obstacle avoidance control of UAV. In view of the problems that the classical artificial potential field algorithm is easy to fall into local minima and the trajectory is tortuous, this paper analyzes the boundary repulsion potential field function in the actual scene and corrects the trajectory in advance based on the priori idea. Aiming at the problems of nonlinear and sensitive to disturbance of the UAV system, a hybrid position and attitude tracking control method based on the cascade PID-ADRC is proposed. The simulation results show that after adding white noise disturbance, the UAV can still avoid obstacles and reach the specified target position smoothly, and its attitude tracking is fast and stable with good anti-disturbance capability.
An Improved Whale Optimization Algorithm Based on Nonlinear Convergence Factors and Differential Evolution

A whale optimization algorithm combining a non-linear convergence factor and differential evolution is proposed to address the shortcomings of the Whale Optimization Algorithm (WOA) in terms of insufficient search capability and the tendency to fall into local extremes. The exploration and exploitation capabilities of the WOA are coordinated through an improved non-linear convergence factor, and the global optimization-seeking capabilities are enhanced through differential evolution. A total of 10 single-peaked and multi-peaked benchmark functions were tested, and the mean of the convergence results from 100 runs are give, as well as the success rate of the search. The results are compared with the WOA and the WOA improved by the adaptive weighting strategy alone, showing that the improved WOA is significantly better than the compared algorithm in terms of the merit-seeking ability and convergence speed. The proposed algorithm is applied to the state of charge estimation of Li-ion batteries, and verified with smaller error.
Multi-Ellipse Fitting Based on Kalman Filter

This paper proposes a Kalman-filter-based algorithm for fitting multiple ellipses. Specifically, the curve in Cartesian coordinates is first transformed into an extended curve in polar coordinates to obtain ordered data. Then, the Kalman filter is used for clustering to select points belonging to the same candidate ellipse. Finally, the parameters of each ellipse are solved using the least squares method to achieve ellipse fitting. Overall, the proposed algorithm effectively fits multiple ellipses while avoiding a large number of ineffective samples and maintaining a low computational load. Moreover, the numerical simulation results demonstrate that the proposed method has good robustness against outliers.
A Hybrid GA-APF 3D Trajectory Planning Method for Large-Scale UAV Formation Systems

In the field of trajectory planning, the generation of a feasible trajectory ensures flight safety for unmanned aerial vehicle (UAV) formations and minimizes mission execution costs, making it a crucial and actively researched topic. To generate a feasible trajectory in large-scale UAV formation systems, this paper proposes a novel Hybrid Genetic Algorithm-Artificial Potential Field (GA-APF) method for trajectory planning. The genetic algorithm (GA) is modified by introducing a new selector and mutation operator to enhance the efficiency of assigning mission targets. Furthermore, the artificial potential field (APF) method is improved by incorporating a three-layered repulsive potential field and modifying the calculation of the attractive potential field to ensure safe and accessible paths for all UAVs. Finally, the trajectory is smoothed through the use of the B-Spline algorithm. Simulation experiment on the Hybrid GA-APF method using 1000 UAVs shows that the method not only reduces the trajectory length by more than 60% but also generates safer trajectories. Using trajectories generated by the method on a real-world flight platform can enable large-scale UAV formations to complete their missions safely.
A Proximal Gradient Method for Regularized Deep Neural Networks

In this work, a regularized minimization problem is proposed for training the DNNs with a cross entropy loss function. We adopte an hnorm penalty to it when the activation functions are convex and non-smooth. We prove that the optimization problem can act as an exact penalty model whose global minimizers are also solutions of the original regularized model. Due to the convexity and boundedness of the feasible set of the optimization problem, a proximal gradient algorithm is adopted to solve it. Specifically, a smoothing technique is applied to the activation function when it is non-smooth, e.g., ReLU and leaky ReLU. Compared with the existing widely used SGD methods, its efficiency and robustness is illustrated through comprehensive numerical experiments.
An Instance Segmentation Algorithm Based On Signed Distance Field

As a high-level visual task, instance segmentation aims to distinguish foreground from an image and achieve pixel-level object segmentation. With the rapid development of instance segmentation in recent years, the rich image information extracted from the task of instance segmentation can be widely applied in various fields of work and life. It is a key technology in medical image processing, unmanned driving and other application scenarios. Aiming at the high requirement of segmentation accuracy in application scenarios, an improved instance segmentation algorithm based on signed distance field is proposed in this paper for the first time. Based on the Mask R-CNN, the idea of scale arrangement and cross-scale connection is introduced into the feature extraction network to replace the ResNet + FPN, which further improves the positioning accuracy in the object detection stage. The boundary of instances in the instance segmentation task is always closed, therefore, the signed distance field branch proposed in this paper is introduced after mask branch, which can be useful for predicting the boundary contour of the instance more accurately and solve the problem of rough boundary segmentation in the segmentation task. Experimental results show that the proposed method has better segmentation effect on COCO2017 data set than the benchmark method.
A Study of Calligraphy Font Generation Based on DANet-GAN

In recent years, the generation of calligraphic fonts has set off an upsurge in the field of deep learning research. But the original calligraphers are less preserved due to various problems such as long time. A large number of calligraphic-style fonts are generated through existing authentic relics. Creating a complete set of calligrapher font libraries is of great value for inheriting Chinese culture. At present, all kinds of deep learning methods and traditional methods need a lot of manpower and material resources. And the generation quality is not high. To facilitate the automatic generation of calligraphy characters, this paper proposes a calligraphy character generation system that adds an attention mechanism to the generative antagonism network. In this paper, the Dual Attention Network(DANet) is inserted between the coding layer and decoding layer of the basic zi2zi network. It can improve the generation efficiency of calligraphy characters and generate a large number of calligraphy fonts. In this paper, ablation experiments and comparison tests were carried out on three calligraphy data sets, namely Wen Zhengming, Ouyang Xun, and Liu Gongquan. The generated font structure is complete, the details are clear, and the basic realistic calligrapher's original. The index is better than other models and algorithms, which verifies its effectiveness and universality.
Channel Equalizer Design for Continuous Wave Mud Pulse Based on OS-ELM

Mud pulse transmission technology is one of the core technologies for measurement while drilling. In the process of continuous wave mud pulse signal up to the ground, there will be large amplitude attenuation and inter-code interference caused by the multipath effect. Therefore, an adaptive decision feedback equalizer based on online sequential extreme learning machine (OS-ELM) is proposed in this paper, where the OS-ELM is used as the feedforward filter of the equalizer and the feedback filter is based on the least mean square algorithm for decision feedback. First, a data set is built using a simulated mud pulse equivalent baseband signal, which includes a useful distortion-free signal and a corresponding distorted signal. Then the designed equalizer is initialized and updated online using the created dataset to obtain equalizer parameters that have good distortion correction capability. Finally, tests are performed on the validation set samples with different signal-to-noise ratio (SNR). The test results show that the bit error rate (BER) of the mud pulse signal processed by the designed equalizer is greatly reduced compared to that of the conventional equalizer. It can be seen that the designed equalizer compensates and corrects the signal attenuation and inter-code interference significantly, which verifies the effectiveness and feasibility of the method in this paper.
HNSRRT*:A Path Planning Algorithm Based On Heuristic Non-Uniform Sampling Method In Complex Obstacle Environment

The traditional sampling-based algorithm such as Rapidly Random-exploring Tree (RRT) and various varieties have achieved tremendous success in the area of path planning. However, their excessive exploration in the state space leads to long time to find the optimal solution, large memory usage and cannot guarantee the quality of the planned path(generally evaluated by the cost of search time and the length of path) in sophisticated space. In this article, we propose an optimal path planning algorithm based on heuristic non-uniform sampling, namely the HNSRRT*, which successfully plans path in complex obstacle environments with optimal length and minimum time cost. The HNSRRT* utilizes heuristic function to generate non-uniform sampling distribution by Gaussian distribution,and constraints on sampling points can reduce the time wasted and path length increase caused by excessive exploration. We test the proposed HNSRRT* in 2D and 3D complex obstacle environment,comparing it with the three traditional sampling-base algorithms. The simulation results indicated that the effectiveness and efficiency of HNSRRT* and have an obvious improvement in term of time cost, path length compared with the existing algorithms.
Fall-LSTM: A Fall Detection Network Based on Spatio-Temporal Location Module

Fall events have unique dynamic features, which are not fully utilized by existing fall detection methods. Based on video understanding, we propose Fall-LSTM to learn such features pertinently without additional inputs. Fall-LSTM is composed of CNN-LSTM framework and two excitation modules i.e., Spatial Attention Module (SAM) and Temporal Location Module (TLM). SAM provides spatial constraints on motion for feature layers through foreground extraction and spatial pooling. TLM emphasizes frames with high probability of fall events to LSTM by inferring the rate and trend of motion in clips. Experimental results show that our proposed modules significantly improve the performance of LSTM model, outperforming the state-of-the-art methods on two public Fall Detection Datasets i.e., Le2i and UR.
A Pursuit-Evasion Game on a Real-City Virtual Simulation Platform Based on Multi-Agent Reinforcement Learning

In this paper, we investigate the problem of pursuit-evasion games for quadrotors in an urban obstacle scenario. At first, a high-fidelity simulation environment is designed based on realistic urban scenarios. This environment contains a powerful physical engine and real-time interaction technology with the algorithm side, guaranteeing the consistency of the simulation environment and the real world. Afterwards, the Multi-Agent Soft Actor-Critic (MASAC) method is proposed. Soft Actor- Critic(SAC) is combined in a centralized training distributed execution (CTDE) framework to guarantee stable policy exploration. Furthermore, the maximum entropy model is adopted to increase the stochasticity of the given policy. Thus, the robustness and performance of the proposed policy can be improved. Finally, the experiments are implemented to demonstrate the effectiveness of MASAC compared to other popular methods.
Tracking A Maneuvering Target Using Interacting Multiple Model Algorithm based on Trajectory Function of Time

This paper addresses maneuvering target tracking problem of the follow-up system based on the curve fitting framework. In our work, we extend the trajectory function of time (T-FoT) method to account for maneuvering target tracking field. To tackle the problem that the maneuvering target can not be accurately modeled, we have proposed the interacting multiple model algorithm based on trajectory function of time (IMM based on T-FoT). Our proposal avoids the difficulty for Markov jump (including maneuvering) and process noise modeling, yielding a data-driven tracking approach using little a-priori target model information. Simulation is conducted to demonstrate the effectiveness of IMM based on T-FoT, in comparison with IMM based on KF approach.
Traffic Sign Detection Algorithm Based on CA-YOLOV4-tiny

Autonomous driving technology is gaining popularity, but most deep learning algorithms for target detection of traffic signs suffer from a large number of parameters and a long inference time. This paper proposes a CA-YOLOV4-tiny traffic sign detection algorithm based on the YOLOV4-tiny network, feature extraction is performed by incorporating the attention mechanism, mosaic data is used to improve the generalization ability of the model, and label smoothing is used to prevent overfitting. The mAP on the TT100K traffic sign dataset is 81.71%, which is 6.77% higher than that of YOLOV4-tiny. The model is deployed on the NVIDIA Jetson Xavier NX artificial intelligence computing platform with a frame rate of 28 FPS, and the frame rate is 60 FPS after the model is quantif ied and accelerated using TensorRT. The experimental results show that CA-YOLOV 4-tiny can significantly improve the model detection accuracy without reducing the reasoning speed, and the reasoning speed after quantization has also been significantly improved, resolving the vehicle-side target detection network's problem of excessive computing power and slow reasoning speed.
Disentangling Representations in Linear Causal Models with Few Paired Samples

Disentangled representation learning aims to extract high-level factors from low-level observations, while ensuring that each component of the learned representation is meaningful on its own. Although these factors are often assumed to be independent, they may be causally related in practice. In this work, we investigate the problem of disentangling representations of observations generated by a latent linear causal model with obervational data and a few paired samples. To be specific, we consider a linear structural equation model with additive Gaussian noise, and we assume that we have enough observational data and a few paired samples in which only one factor has changed in each pair. The disentangled representation learning problem is solved by first constructing an intermediate representation which represents the factors of variation of the observational data with standard Gaussian prior. Next, the relation between the intermediate representations and the latent variables is identified by solving a matrix equation or a matrix optimization problem. We prove that, under certain suitable assumptions, our representation model identifies the true representation up to a diagonal matrix, and we provide a learning algorithm to approximate the identifiable representation based on variational autoencoders.
A Novel State Estimation Algorithm for Nonlinear System with Unknown Sensor Input

To deal with the state estimation problem for nonlinear system with unknown sensor input, in this paper, a novel state estimation problems without considering unknown sensor input for nonlinear system is considered. That is, it is no need to estimate the sensor noise and the filter is carried out in the sense of unbiased minimum mean square error estimation. To show the effectiveness of the proposed filter, the numerical realization on the basis of spherical-radial cubature rule is given, and a nonlinear model with unknown sensor input is applied to test the effectiveness of the proposed algorithm. What's more, the superiority is shown through the simulation results.
Application of YOLOv7 in Remote Sensing Image Target Detection

Remote sensing images are characterized by small targets, complex types, and large variations between different and similar targets, resulting in poor target detection and uneven detection performance of multiple types of targets. To reduce the error detection and omission detection of remote sensing image targets and improve the detection accuracy of multiple targets, an improved feature extraction network YOLOv7 detection model is proposed. Based on the YOLOv7 algorithm, the model was experimentally studied using the DOTA remote sensing image dataset. A new ELAN module is constructed using full-dimensional dynamic convolution ODConv, which improves the utilization of multi-dimensional information in nuclear space and reduces computation volume. Integrates the multi-headed self-attentive MHSA into the backbone network to better distinguish background information. Our proposed network model has 4.6%, 15.9%, 4.1% and 3.7% mAP improvement compared to YOLOV3-CSP, YOLOV4-CSP, Resnet50 and X50-CSP, respectively. Most importantly, Compared to the original YOLOv7 algorithm, the parameter settings are reduced by 6.6%, and the average detection accuracy(mAP) improved by 1.44%, better balance between accuracy and the number of parameters while meeting real-time detection.
Improved Mixed Gaussian Model for Background Subtraction Based on Color Channel Fusion

Motion object detection, which has a wide range of applications in video surveillance systems, extracts motion objects from the video. Gaussian Mixture Model (GMM) has achieved widespread success in motion object detection because of its good performance. However, it simplifies color channel information or directly uses grayscale information. If the multi-color channel information can be jointly used, it is expected to obtain better results in complex scenes. Therefore, we design the multi-color channel voting GMM by jointly using the multi-color channel information and introduce soft voting based on soft decision to further strengthen the use of information. Experimental results show that the multi-color channel voting GMM proposed in this paper can well detect motion objects in complex scenes. Compared with original GMM algorithms, multi-color channel voting GMM has a better F-measure metric in the complex scenarios.