Reviewerly/exHarmony huggingface dataset:
A Security and Trust Protection Framework for Open CNC Production Line

With the further integration of information technology and industry, the computer numerical control (CNC) production line has gradually changed from the original isolated and closed mode to an open one. It faces not only internal threats, but also threats from the Internet. Starting from the characteristics and cybersecurity problem analysis of open CNC production lines, this paper proposes a security and trust protection framework based on trusted computing 3.0 and the concept of defense in depth, where a tri-element peer architecture is introduced to realize two-way authentication between the access device and the platform and an active immunity scheme for open CNC system is designed to ensure the trust from the source, and the trusted NC-Link agent/adaper are utilized to connect the heterogeneous equipments trustedly in the production line. The proposed framework can improve the information security level of the open CNC production line while maintaining its openness.
Enabling Inter-Agent Transfer for Multi-Agent Learning System by Incorporating Role Reversal

This work focuses on inter-agent transfer for multi-agent reinforcement learning. For a learning system consisting of multiple agents that cope with similar situations, incorporating inter-agent transfer could significantly improve the learning efficiency since the trajectories from others also benefit local policy iterations. However, experience sharing is typically not straightforward since the agents may have different sensors and observations. In order to transfer trajectories among agents, we propose to incorporate role reversal, which maps the observations of different agents and augments the experiences by exchanging their viewpoints. In this way, each agent learns from the trials of both itself and others. We further conduct simulations to validate the effectiveness, where the proposed algorithms surpass the baseline algorithms by a remarkable margin in all the situations. Importantly, our method can be integrated into all modern off-policy multi-agent reinforcement learning algorithms to pursue better performance.
Handwritten Manchu Historical Document Segmentation with Anisotropic Gaussian Kernel

It is key technology for handwritten historical document image analysis to segment text lines. There is no doubt that it is very difficult for text line segmentation owning to varied forms of handwritten Manchu historical document images. A novel method for handwritten Manchu historical document segmentation is presented. Initially, this method extracts the blob lines of the text lines by anisotropic Gaussian kernel and obtains connected components by contours. Then, it breaks the adhesion region between lines which is obtained by blob lines merging. Finally the detected text line and connection components combine to form the initial text line region, and text lines segmentation is achieved by boundary area growing. Results of segmentation for heterogeneous documents are provided, and the proposed method is compared with projection profit and seam craving methods in the same settings. The method is good at handling the skew and adhesion Manchu text lines. Tested on dataset of handwritten Manchu historical documents, the recall of 97.57% correct lines is achieved by the proposed method.
Reinforcement Learning-based Hierarchical Obstacle Avoidance Strategy for Fixed-wing Aircraft

Obstacle avoidance is a crucial issue to enhance the safety of aircraft. Aircraft usually need to avoid a drop in altitude and keep a set course while avoiding an obstacle. In this paper, a hierarchical obstacle avoidance strategy is proposed to address obstacle avoidance, the drop in altitude, and course keeping simultaneously. The hierarchical obstacle avoidance strategy integrates a high-level reinforcement learning-based navigator and a low-level attitude controller. Experiments are conducted in X-Plane, a flight simulator, to evaluate the proposed hierarchical obstacle avoidance strategy.
Research on Medical Image Mosaic Using Immunity Affinity Action Range Theory

Image Mosaic technology which is widely used in Industry, Medicine and other fields has been in good development. However, when stitching the images which have similar texture or have similar local features, there are still some issues remaining to be further studied. This paper proposes a concept of Immunity Affinity Action Range (IAAR), and presents an algorithm which is named Immunity Affinity Action Range Clonal Selection Algorithm (IAARCSA) by using IAAR. Susan algorithm is firstly used to find the feature points of images, afterwards it applies IAARCSA to determine the matched positions in images, then image mosaic is completed. This paper gives multi-forms of IAAR, which can help to choose IAAR according to the different characteristics of the mosaic image. Simulation results showed that the quality of the image mosaic is guaranteed as well as the efficiency of image mosaic is improved.
An optimal trajectory planning algorithm for T-shaped acceleration curve with non-zero acceleration at the beginning and end

In order to solve the problem that T-type trajectory planning curve can not completely replace polynomial trajectory planning,this paper proposes an S-type programming algorithm based on the optimization algorithm with T-type acceleration. The algorithm solves the complex and cumbersome problem of time planning of T-shaped acceleration and deceleration curve with non-zero acceleration and velocity at the beginning and end. Taking into account the characteristics of fast acceleration of trajectory planning curve, it can not only solve the case where the initial and end velocities are not zero, but also solve the case where the initial and end accelerations are not zero. The algorithm is based on the analysis of the characteristics of acceleration stage, constant speed stage and deceleration stage. When the highest speed of the curve is determined, the time of all speed segments is also determined. The concept of maximum reachable speed of trajectory is proposed. The complex multivariate nonlinear programming is transformed into a single convex function. The algorithm is simplified by using the symmetry of the variation forms in acceleration stage and deceleration stage. The maximum achievable velocity is solved by dichotomy, Newton iterative method and gradient descent method. The experiment shows that the solving efficiency of Newton iterative method is 40% higher than that of dichotomy method. Compared with the quintic polynomial programming algorithm, the overall planning time is reduced by 9.6% and the tracking error is reduced by 15.9%.
Pedestrian Detection for Autonomous Vehicles Using Virtual-to-Real Augmentation

Annotated data are essential to the success of training deep neural models for autonomous driving. Practically, it is both expensive and time consuming to collect and annotate plenty of data driving cross the city. It is even harder to cover the corner cases involving rare pedestrian poses with occlusions. To tackle this problem, we propose a data augmentation method for pedestrian detection. First, we take a SMPL model to generate pedestrians with a variety of poses and textures. Second, we insert the rendered pedestrians into images with real traffic background according to vanishing point. Occlusions can also be set easily by taking use of segmentation annotations of original real dataset. We evaluated the effectiveness of the generated synthetic dataset using multiple detectors. Results of three experiments shown that detectors trained with mixture of synthetic and real data outperform those trained on only real datasets.
A model-free block-based camera calibration method

Camera calibration is crucial for machine vision application. To deal with the limitation of complex distortion modeling and requirement of multiple images at different viewpoints, this paper put forward a model-free block-based camera calibration method. The Monte Carlo simulation is first introduced to reduce the affection of ununiform illumination. Then, to precisely estimate the distortion degree at different image positions, the corner is detected and grouped according to the feature of chessboard plate. Finally, the block-based perspective transformation is utilized to reduce the imaging error aroused by camera manufacturing and installation. Experiments prove that the proposed method is stable and performs better than Zhang’s method at different situations.
PMSM Deadbeat Predictive Current Control Based on Extreme Learning Machine

In order to strengthen the tracking performance and robustness of permanent magnet synchronous motor (PMSM) system, a deadbeat predictive current control (DPCC) based on extreme learning machine (ELM) is come up. Since PMSM is susceptible to uncertainties such as external disturbances and parameter changes, the uncertainty factors are introduced in the mathematical model. The uncertainty of the system is approximated by the ELM, then the speed tracking of the permanent magnet synchronous motor is realized, and the stability is certificated by establishing the Lyapunov function. In addition, DPCC method of PMSM is proposed, which is equivalent to high-gain proportional control and improves the performance of the PMSM. Finally, the simulation experiments are carried out in nominal case and parameter mismatch case respectively, through the comparative study of system simulation, the results indicate that contrast with the traditional control method, The ELM-DPCC proposed in this paper has better speed tracking performance and robustness.
Fusion of Improved A* Algorithm and Dynamic Window Approach for Path Planning

Path planning is an important technique for mobile robots, which can effectively map out a safe path, whether in an open field or in a complex and changing environment. To address the problems of many inflection points and irregular routes in traditional path planning algorithms for mobile robot, this paper proposes a path planning approach that incorporates the improved A* algorithm and the dynamic window approach. First, the algorithm is based on the jump point search algorithm, which extends path finding by finding jump points, thus reducing unnecessary nodes and ultimately achieving the effect of shortening path finding time. Then, the generated paths are smoothed using the gradient descent method to make them smoother and more natural; finally, the improved A* algorithm and the dynamic window approach are fused. This makes the planned path smoother and the obstacle avoidance more accurate, which not only reflects the good path planning ability of the mobile robot, but also meets the practical needs of the mobile robot.
Enhanced ant colony algorithm with dynamic updating mechanism for mobile robot path planning

This paper is devoted 'to concerning with the path planning problem for mobile robot with dynamic updating strategy. First, the heuristic function is modified to adjust the priority of global searching efficiency and convergence speed with the aid of traditional ant colony algorithm. Then, the dynamic updating mechanism of evaporation coefficient is introduced to make the algorithm exclude the long path and accelerate the process of searching the optimal path. Finally, a simulation example is presented and simulation results are compared to evaluate the efficacy of the proposed algorithm.
Research on Obstacle Avoidance Planning of Serpentine Manipulator Based on Particle Swarm Optimization

Compared with traditional manipulators, serpentine manipulator have the advantage of high flexibility and have broad prospects in exploring confined spaces. Aiming at the problems of large amount of calculation and poor obstacle avoidance effect in motion planning of serpentine arm in complex space environment. In this paper, combined with the particle swarm algorithm, a "head traction" path tracking method is designed, so that the rope-driven serpentine manipulator can efficiently move along the planned path in complex terrain. In the method of this paper, the obstacle modeling is completed first, and then a serpentine arm path planning algorithm based on particle swarm optimization is further designed. Use the planned path as the path point tracked by the serpentine manipulator "head". As the manipulator moves forward, all joints follow the path traveled by the manipulator's "head". In the final stage, in order to ensure that the manipulator can accurately reach the target point, an end position adjustment algorithm is designed. Through the simulation experiment of the rope-driven serpentine manipulator based on 21 degrees of freedom, it is proved that the method can successfully make the serpentine manipulator move in complex space, and has high computational efficiency and high precision, which is well verified the validity of the study.
Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly available.
Research on Path Planning of Substation Inspection Robot Based on Improved A* Algorithm

In order to increase the robot’s productivity while inspecting the substation, a new and improved version of the A* algorithm is proposed as a solution to the issues with the original version, such as the slow path finding speed, the many bending points of the planned path, the roughness, and the ease with which the robot can get too close to obstacles.To increase the algorithm’s efficiency in selecting a route, a new heuristic function is first established to provide a dynamic, configurable weighting factor. As a second step, configure the collision penalty function such that a repulsion penalty is triggered if the robot’s Euclidean distance from an obstacle is less than the obstacle’s action range. This keeps the robot and the obstacle from coming into dangerously close proximity to one another. In the end, a route smoothing approach is presented, and the produced path is afterwards smoothed using a bezier curve. The experimental findings confirm that the modified A* algorithm increases productivity, satisfies the needs of robot route planning, and produces the intended impact.
Differential Learning and Parallel Convolutional Network for Skeleton-Based Action Recognition

Graph convolution network (GCN) has recently played a positive role in improving the accuracy of skeleton-based action recognition. Many GCN methods have reached a high accuracy. However, the lightweight of network model has recently become a major concern. Pointing at the problem, this paper introduces a lightweight network, a Differential Learning and Parallel Convolutional Network (DL-PCN), which is based on Semantics-Guided Neural Networks (SGN). The network is mainly composed of Differential Learning Module (DLM) and Parallel Convolutional Network Module (PCN). DLM is characterized by the feedforward connection, which improves the experiment accuracy. PCN can learn the multi-dimensional information of original skeleton data by the parallel connection of GCN and convolutional neural network (CNN). Considering the test accuracy of action recognition and network parameters, our network achieves the comparable performance on the NTU RGB+D 60 dataset and the NTU RGB+D 120 dataset.
Fingertip detection method based on HOG feature voting of hand contour components in complex background

In order to improve the accuracy of fingertip detection and reduce the influence of complex environment, severe illumination and partial occlusion on fingertip detection, this paper proposes a fingertip detection method based on voting of hand parts. First, the edge HOG features of the complex background images and the edge HOG features of the hand images filtered by the effective feature filter are used to train the voting classifier, and the filtered edge HOG feature and its corresponding centripetal offset vector are saved as a feature dictionary. Edge HOG effectively reduces the influence of environment and illumination on the algorithm by virtue of its insensitivity to illumination change and texture, and feature screening reduces the influence of invalid features on the classifier and reduces the difficulty of classification. Secondly, in the process of fingertip detection, the edge features of hand images in complex background are extracted and input into the voting classifier to obtain effective features. Then, the KNN algorithm is used to match the effective features with the feature dictionary to obtain the fingertip detection set. The Meanshift algorithm is used to obtain the final detection result from the feature set. This method based on partial feature voting can effectively deal with the occlusion problem. Finally, the proposed algorithm is compared with different fingertip detection methods. The experimental results show that the proposed algorithm has better performance in the face of complex background, illumination transformation and occlusion problems. The detection accuracy reaches 99 % and the predicted RMSE is kept within 5 pixels.
Study on Terrain Navigation Technology for Cruise Missile Based on Laser Echo Waveform Matching

Terrain matching navigation cannot be implemented effectively for the cruise missile in the flat terrain area, which is very disadvantage for the selection of terrain matching area. In this paper, a new terrain navigation technology for the cruise missile based on laser echo waveform matching is studied. By extracting the characteristics of the laser echo waveform from the ground target and matching with the waveform data in the database, the attribute and location of the ground target are determined. The flight route of the missile can be further corrected according to the position of the target in the map. This technology can expand the selection range of terrain matching area, lower the requirement on the height fluctuation of the terrain and reduce the difficulty of planning terrain matching area for the cruise missile, and has a good military application value for improving the performance of cruise missile.
Trajectory Planning Method for Driverless Sweeper in Unstructured Environment

In the paper, we propose an approach for full coverage path planning and obstacle avoidance trajectory planning in the unstructured environment, applied to the driverless sweeper with Ackermann-like steering. The approach uses the boustrophedon cell decomposition method to solve the full-coverage path planning problem while using DWA (Dynamic Window Approach) and A* and a finite state machine to solve the planning problem of obstacle avoidance trajectories. A kinematics model of the Ackermann-like steering structure sweeper is established to make the obstacle avoidance trajectory satisfy the kinematic constraints. The method designs a corresponding trajectory evaluation function to make the DWA applicable to the sweeping scenario. And a finite state machine model is designed for the decision problem in obstacle avoidance. Finally, using a physical sweeper of 1.5m x 1.0m x 1.2m size in a factory environment verifies the effectiveness of the proposed method.
Path Planning for a Greener Air Traffic Operation using RRT-based Algorithm

Due to population explosion and immoderate industrialization, the impacts of the greenhouse effect are intensifying. To reduce emissions, the administration, air traffic management (ATM) institutions, airlines, and aircraft manufactures are committed to finding a more efficient and greener air traffic operation mode. The high precision trajectory-based operation (TBO) which puts forward the requirements of capability matching and coordination between aircraft and ATM systems has great potential. In this paper we present a RRT-based algorithm for the greener flight course planning. Algorithmic structure of the RRT is firstly introduced. We then illustrate the 3D workspace, constraints of the large civil aircraft, and obstacles in the path planning task. A 3D RRT algorithm is particularly developed for the path planning mission. Experimental tests using both simulated and real weather data are performed, which justifies the effectiveness of the RRT-based path planning algorithm developed in the paper.
Self-supervised Lightweight Line Segment Detector and Descriptor

Image line segment features can be used as a complement to points for geometric computer vision tasks. Previous deep learning-based line segment feature extraction is difficult to perform real-time inference in environments with limited computational resources due to the huge model size and computational cost problems. In this paper, we introduce the joint detection and description of line segments using lightweight networks in resource-constrained environments. We use a lightweight backbone as an encoder and design a top-down architecture to fuse multi-layer features, which is split into three branches for different tasks. Moreover, thanks to the self-supervised training scheme, our model can be trained on dataset without ground-truth line segments. In the experiments, our approach achieves competitive performance with the fastest model inference speed.
Uncalibrated visual servo algorithm based on RBF neural network optimizing maximum correntropy Kalman filter

A radial basis function neural network (RBF) modified maximum correntropy Kalman filter algorithm (RBFMCKF algorithm) is proposed for robot uncalibrated visual servo positioning control. Because there may be non-Gaussian measurement noise in robot visual servo, the maximum correntropy criterion (MCC) is introduced into KF framework to suppress the impact of non-Gaussian noise on filtering accuracy, and then RBF neural network is used to adjusting the error produced by the maximum correntropy Kalman filter (MCKF) algorithm. The simulation results show that the proposed RBFMCKF is effective and can effectively suppress the influence of non Gaussian noise on the filtering accuracy. It provides an idea for further optimizing the robot’s uncalibrated visual servo positioning control.