Reviewerly/exHarmony huggingface dataset:
A Novel Local Trajectory Planning With Adaptive Extended Step Length for Autonomous Vehicles

This study proposes a novel and robust local trajectory planning for autonomous vehicles driving in open space. We improve the existing state space sampling local trajectory planning in order to utilize the higher-order information of the environment to generate safer trajectory and adaptively change the step length of each plan. At the same time, due to the incomplete consideration of the existing evaluation function we present a new evaluation function. The result of the simulation experiments shows that the new proposed local trajectory planning is more capable to plan safer trajectory than the existing method and is able to adjust the appropriate planning step length for each planning cycle according to the environment and the state of the autonomous vehicles.
Edge Refinement Exploration for Salient Object Detection

Recently, salient object detection (SOD) has made significant progress. However, it is still challenging to make accurate predictions around the boundary regions. We introduce an Edge-Guided Salient Refinement Module (ESRe) to increase the detection accuracy near object boundaries, which extends Domain transform (DT) technology from the field of one-dimensional (1-D) signal processing to two-dimensional (2-D) image processing. By feeding into the coarse saliency map and the edge probability map, ESRe performs a convolution operation on the coarse saliency map to calculate the correction value. And then, ESRe refines the coarse saliency map to be better aligned with object boundaries, guided by the edge information which can be seen as the prior knowledge. Specifically, the edge probability map controls the relative contribution of the coarse saliency map and correction value to the refined saliency map. Extensive experiments on popular benchmarks demonstrate the E-Sal achieve competitive performance compared with state-of-the-art methods.
Interval RTD-A Control for Non-square Systems

Aiming at interval control of multi variable non-square systems, this paper proposes an interval predictive control algorithm for thin systems based on the RTD-A algorithm. The set interval is introduced into the optimization index and the control variable is obtained in real time based on gradient descent algorithm. By adjusting the weight to restrain the interference and reduce the energy consumption of the controller with small actions, the actual control output can be avoided from exceeding the expected range to the greatest extent. Simulation results show the effectiveness of the proposed algorithm.
Cooperative Object Transportation for Second-Order Multi-Robot Systems in Dynamic Environment

Cooperative transportation has become popular in recent years due to the development of multi-robot system. The whole transportation task is composed of several subtasks like formation control, team navigation, and dynamic obstacle avoidance. In this paper, we mainly focus on optimization-based control for cooperative transportation task. First, we describe the second-order dynamic of multi-robot system, and then define constraints for each subtask to establish the form of optimization problem. Second, we propose a controller based on hierarchical quadratic programming method to cooperate subtasks with different priorities. Finally, we design simulations to validate the effectiveness of our controller.
A Continual Learning Method for Reducing Class Interference Based on Replay

Although deep neural networks perform well on many individual tasks, they suffer from catastrophic forgetting when learning new tasks continually. Recently, various continual learning methods have been proposed, and some approaches based on replaying memory data achieve promising performance. Maximally Interfered Retrieval is a strong replay-based baseline, however, exists class interference due to class imbalance and lacks the ability to generalize to the real class-incremental scenario. In this paper, aiming at these problems, we design Class cumulative Classifier to replace the shared output layer of the original network, which makes it closer to practical applications, and Class balanced Buffer to address the class imbalance of stored samples. In addition, we propose Retrospect strategy to further improve the accuracy. Experimental results on benchmark datasets show that our method outperforms several strong baselines and is more suitable for complex datasets with more classes.
An Improved Algorithm for Elliptic Curve Digital Signature

The Elliptic Curve Digital Signature Algorithm (ECDSA) is an elliptic curve variant based on the Digital Signature Algorithm (DSA). Based on the classical ECDSA scheme, a large number of improved algorithms have been proposed, but most of them use only one private key. Once the unique private key is leaked, security is not guaranteed. In this paper, we propose a new enhancement of the ECDSA scheme by introducing two private keys for the sender. We eliminate the inversion operation in both signature and verification processes. And in our signature equation, the number of model multiplications reaches the minimum number of zeros. Mainly relying on the mathematical problem Elliptic Curve Discrete Logarithm Problem (ECDLP), our scheme is resistant to private key attacks and provides unforgeability. Meanwhile, our scheme partly counteracts the risk of private key leakage. In this way, our scheme provides higher security compared with existing schemes.
A Collision Avoidance Planning Algorithm in Cluttered Environments with Moving Obstacles

This paper investigates the collision avoidance planning problem for Mecanum-wheeled robot in cluttered environments with moving obstacles. The challenge lies in that the motions of obstacles are not available and hard to be predicted. First, a IMM-based algorithm is developed to obtain motion model and states of moving obstacles. Then, an optimization-based planning algorithm is proposed to achieve collision avoidance in which the planning problem is transformed into an unconstrained optimization problem, which can be solved by g2o-framework. The simulation experiments are performed to show the effectiveness of the proposed planning algorithm.
Consistent Anchor Induced Multi-View Deep Matrix Factorization

Deep Matrix Factorization (DMF)-based multi-view learning approaches have demonstrated great potential in learning hierarchical semantics of multi-view data, but have paid limited attention to effectively exploiting the consensus attribute of different views. To further enhance their ability for multi-view data analysis, a Consistent Anchor induced Multi-view DMF method is proposed in this paper, which facilitates integrating information from different views by selecting consistent anchors on the data to reconstruct the relationship between instances. An effective graph regularization is also introduced to learn the optimal manifold of the multi-view data. Experimental results on five benchmark datasets show that the proposed method outperforms the state-of-the-art multi-view learning approaches.
A Hybrid Algorithm for Collision-Free Navigation of a Non-Holonomic Ground Robot in Dynamic Environments with Steady and Moving Obstacles

This paper proposes a novel hybrid navigation for collision-free movement in a dynamic environment. It considers vehicle operating in obstacles that are both stationary and moving. The proposed method combines global path planning with reactive methods to guarantee safe navigation. This hybrid method provides an optimal solution for safe navigation in a partially-known, dynamic environment by combining the benefits of global and reactive navigation to overcome the limitations of relying primarily on one of them. Computer simulations are conducted to validate the performance of the proposed method.
Object Detection in Drone Video with Temporal Fusion Network

Images acquired by Unmanned Aerial Vehicles (UAV) suffer from serious deterioration. A typical solution for UAV based image object detection is to enhance target appearance in current frame by aggregating the features in neighboring frames. Simple feature aggregation methods will introduce interference of background into targets. To deal with the problem, a simple but effective Temporal Fusion Network (TFN) is proposed. The key idea is to use separable 3D convolutions and deformable convolutions to align features in different frames. TFN works as an add-on module to bring existing static object detectors to high performance video object detectors with negligible extra computational cost. Exhaustive experiments are carried both on the challenging VisDrone2019-VID dataset and the large scale ImageNet-VID dataset based on different image object detectors, i.e., the anchor-based YOLOv7 detector and the anchor-free YOLOX detector. Particularly, our network based on YOLOv7, termed TFN-YOLOv7, not only achieves a 1.84% increase in mean Average Precision (mAP), but also reaches a running speed of 23 frames per second (fps).
Selective Multi-Scale Feature Aggregation Framework for 3D Road Object Detection

In intelligent transportation, 3D object detection is a key task for autonomous vehicles. It is necessary to consider the multi-scale features aggregation due to the complexity of the point cloud. Based on the perspective, a novel selective multi-scale feature aggregation framework is proposed, which has two modified backbone based on the OpenPCDet framework: Multi-Weights 2D Backbone and Feature Separation 3D Backbone. The former module contains a progressive fusion structure considering the characteristics of the Bird's Eye View (BEV) map in multi-category object detection, and the latter module processes and fuses the location information and the reflection intensity information separately based on the difference between them in raw point cloud. The experiments on KITTI dataset show the progress in multi-category object detection while achieving superior performance on the most common Car category, which reflects the effectiveness of the work.
Improved RetinaNet-Based Defect Detection for Engine Parts

The safety of aviation, vehicles, and ships relies heavily on the quality assurance of engine parts. However, traditional target detection algorithms currently used to detect defects in engine parts do not have a high recognition rate. This paper proposes an improved RetinaNet to address this problem. Firstly, the backbone network ResNet was improved by replacing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7\times 7$</tex> convolutional kernels with stacked <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\times 3$</tex> convolutional kernels, and the C2 layer of ResNet was densely connected to C3, C4, and C5 layers to improve the feature extraction capability. Then we improved the feature pyramid (FPN) structure by adding a convolutional attention module (CBAM) after layers P3 to P5 of the feature pyramid. Finally, the improved RetinaNet was trained on a self-made engine part missing detection dataset, and the model performance was tested separately. The experimental results show that RetinaNet has better performance in detecting engine part defects, but there is a small part of missed detection. The improved model performs well in detecting small targets, and the mean average precision (mAP) reaches 88.4%, which is 1.8% higher than the mAP of the original RetinaNet, furthermore, the average detection time is 0.22s. In the practical scene application, it can quickly and accurately detect defects in engine parts.
3D Keypoint Detection of Lying Human Body Using an RGB-D Camera

3D keypoint detection of lying human body is important to improve the efficiency of mobile rescue robots at the casualty collection point after a disaster (e.g., earthquake and mudslide). In this paper, we propose an efficient method for 3D human keypoint detection in a lying posture using an RGB-D camera. First, we use the current 2D human pose estimation algorithm for RGB images to obtain 2D keypoints of the whole body. We then obtain the final 3D coordinates of the human keypoint by processing the 2D coordinates through a filter of our design and combining the depth information with a coordinate transformation. Experiments show that the proposed method is accurate and fast enough to be used for 3D keypoint detection of lying human body by the mobile rescue robot at the casualty collection point.
MND-GAN: A Research on Image Deblurring Algorithm Based on Generative Adversarial Network

Aiming at the image blurring problem that often occurs in machine vision applications in intelligent transportation and unmanned driving scenarios, an image blurring algorithm MobileNetDeblur-GAN (MND-GAN) based on generative adversarial network is proposed in this paper. The algorithm has the following characteristics: the least square generative adversarial model (LS-GAN) is used for network training to solve the gradient disappearance problem in the traditional GAN network training process; the generator structure is designed based on MobileNet and two scales of input images are used to improve the authenticity of image restoration at different scales; the weights of nonlinear layers at different scales are shared, which greatly reduces the overall reasoning time of the deblurring algorithm; a global discriminator and a local discriminator are combined to construct a dual discriminator structure to improve the adaptability of the model to different scenarios; the loss function uses the combination of perception loss and structure loss to make the deblurring effect more consistent with the visual characteristics of clear images. The performance of MND-GAN algorithm is compared on multiple datasets, and good deblurring effect is obtained. At the same time, the reasoning speed of the network exceeds that of other comparison algorithms, and it can meet the real-time requirements of intelligent transportation and unmanned driving scenarios.
Improved Unbalance Data Classification Performance Based on Fused SMOTE-RBU

To address the extreme imbalance between counterfeit and normal parcels, the courier industry must consider the impact of this factor in the prediction of counterfeit cigarette crime. Otherwise, the performance of the prediction model for unknown parcels will be severely compromised. To improve the accuracy and solve the misclassification problem for this dataset, this paper proposes an algorithm that incorporates SMOTE-RBU. The algorithm first synthesises minority class samples using the SMOTE algorithm, then removes redundant samples from the majority class samples using the RBU algorithm, and finally merges the processed minority class samples with the majority class samples to obtain a balanced dataset that is fed into the classifier for classification prediction. In this study, we tested six different sampling methods (RUS,ROS,Adasyn, SMOTE,SmoteTomek and SMOTE-RBU) and compared five machine learning algorithms (K-Nearest Neighbour, Logistic Regression, Decision Tree, Random Forest, AdaBoost) and four dataset sizes. The experimental results show that the SMOTE-RBU algorithm combined with the RF classifier performs best, with false positives of 0.0165, 0.0179, 0.0126 and 0.0102 on different datasets. Therefore, due to space limitations, only the results of different sampling algorithms combined with the SMOTE-RBU sampling method proposed in this paper for the RF classifier are presented.
GAN-Based Quantum State Estimation

In recent years, deep learning-based quantum state estimation (QSE) has attracted extensive attention, and the generative adversarial network (GAN) is a popular model in the field of deep learning. In this paper, we apply GANs into the field of QSE and design a custom neural network layer which makes the original network outputs satisfy quantum state constraints. It is worth noting that our designed custom neural-network layers are applicable to arbitrary feed-forward neural networks used for QSE. GANs are trained and tested by randomly generated pure state density matrices. Simulation results show that the designed neural network estimator is capable of achieving a fidelity higher than 99% for both eigenstates and superposition states.
The Design of a Nursing Bed Based on Eye Movement Control

Population aging is becoming more and more serious, and more and more elderly people need to be cared for. In this situation, there is an urgent need for nursing beds with multiple functions, especially those that can be controlled by the patient's intention. At present, the existing nursing beds have some supporting functions, such as helping patients lift their backs and flip their bodies through electric devices. However, they have some shortcomings in allowing patients to control the movement of nursing beds. In order to solve the above problems, a nursing bed was built in this study by using an eye tracker and hub motors. The movement of the nursing bed can be controlled through the patient's eye gaze point. Moreover, the nursing bed has four motion modes: low-speed mobile, high-speed mobile, left-turn, and right-turn. This study obtained the following data from ten experiments to prove the nursing bed's rapidity, flexibility and safety. The experimental results show that the average time required by the nursing bed from starting to entering any motion mode is 28ms. The average time required for the nursing bed to switch between two motion modes is 106ms. The average time required for the nursing bed to stop moving from any motion mode is 93ms, which proves nursing bed can switch motion mode flexibly and has the emergency stop function. Besides, this study proved the feasibility of the nursing bed running under a heavy load through three experiments. This study provides a feasible method for patients to control their movement independently.
An Outlier-Robust Kalman Filter Based on the Generalized $t$ Distribution with a Mixed Gaussian Approximation

In this paper, a novel robust Kalman filter (KF) based on the generalized <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> distribution is proposed to deal with measurement outliers. By ultilizing the relationship between the generalized <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> distribution and the Gauss-Inverse-Wishart distribution, a latent random variable is introduced, which helps us derive the accurate analytical form of the posterior density. After that, a mixed Gaussian approximation method is proposed to obtain an accurate approximation of the posterior mean and covariance. We also establish the best Gauss distribution to approximate the posterior distribution under the Kullback-Leibler (KL) divergence. Compared with the current robust KFs, our method is more implementation-friendly, and can achieve a higher accuracy due to the adoption of the latent matrix random variable and the best Gaussian approximation for the posterior distribution under the KL divergence.
Learning Diverse Control Strategies for Simulated Humanoid Combat via Motion Imitation and Multi-Agent Self-Play

Human athletes can coordinate their bodies to achieve exquisite strategies with agile and diverse motions in multiplayer competitions, which is a long-standing challenge for robotics and physically embodied artificial agents. In this paper, we propose a hierarchical learning framework that generates diverse control strategies and agile motion for physically simulated humanoids, which can be divided into two stages: learning basic motion skills and learning high-level competitive strategies. The framework decouples low-level control and high-level strategy learning, where the low-level policy is trained via motion imitation and skill discovery objectives to generate agile motion skills, and the high-level policy is trained via prioritized fictitious self-play to generate diverse competitive strategies. Furthermore, we develop and release the world's first physically simulated humanoid combat environment. We evaluate our learning framework in this environment, and experimental results demonstrate that policies learned by our framework can generate both agile motion skills and diverse long-term competitive strategies.
Training Neural Networks with Momental Bound of Learning Rate

Adaptive algorithms are widely used in deep learning because of their fast convergence. Among them, Adam is the most widely used algorithm. However, studies have shown that Adam's generalization ability is weak. AdaX is a variant of Adam, which modifies the second moment of Adam and has good generalization ability. We propose a new adaptive and momental bound algorithm, called AdaXod, which characterizes of exponentially averaging the learning rate and is particularly useful for training deep neural networks. By setting an adaptively limited learning rate in the AdaX algorithm, the resultant AdaXod can effectively eliminate the problem of excessive learning rate in the later stage of neural network model training and thus stabilize training. Simulation experiments verify that AdaXod eliminates large learning rates during neural network training and outperforms other optimizers, especially on the complex network structures such as DenseNet.
Light-Weight Multi-Target Detection and Tracking Algorithm Based on M3-YOLOv5

The existing multi-target detection and tracking algorithms network models are too large to be deployed on small mobile devices. In order to solve the above problems, a light-weight multi-target detection and tracking algorithm based on M3-YOLOv5 is proposed. Firstly, the YOLOv5s backbone network is replaced by improved MobileNetV3 with sandglass block to reduce the algorithm size. Secondly, the CA attention mechanism is combined to the neck network to improve the small and medium-sized targets detection capability. Finally, DeepSORT is combined to track the obtained detection border information. The application experiment on the NVIDIA Jetson TX2 development board shows that YOLOv5s inference speed is accelerated by 24.6%. The improved multi-target tracking algorithm inference speed is accelerated by 2FPS in comparison with DeepSORT. The algorithm results in less loss of tracking accuracy and enables multiple targets to be tracked quickly, which proves to be suitable for the deployment on small mobile devices.
Double-Swarm Improved Moth-Flame Optimization Algorithm with Golden Sine for Global Optimization

Moth-flame optimization algorithm (MFO) is a swarm intelligent optimization algorithm, which is proposed by simulating the night navigation mechanism of moths. MFO is widely used in various fields since it has a simple structure and easy implementation. However, in the process of MFO search, the population diversity will deteriorate, and premature convergence will occur. To solve the above problems, a double-swarm improved moth-flame optimization algorithm with golden sine operator(DIMFO) is proposed in this paper. The population is grouped by chaos mechanism, and the information exchange between sub-swarms is improved by dynamic recombination. The spiral search and golden sine search are used for the two moth populations respectively, and the Gaussian mutation is utilized to generate flames, which improves the population diversity. In addition, compared with other improved MFO algorithm and advanced optimization algorithms in the classical test set, the results show that DIMFO has advantages in convergence accuracy and speed in most test problems.