Reviewerly/exHarmony huggingface dataset:
Research on Reflector Navigation Algorithm and Optimization Smoothing Critical Path Trajectory

For mobile robots working in dense environments, navigation and path planning are crucial to understanding and reproducing human wayfinding thinking patterns. Firstly, this paper analyzed a hybrid SLAM navigation algorithm based on the reflector and laser constructed by the grid map method. Secondly, in the case of Kalman filter localization loss, this paper uses the generalized least squares inverse method to solve the nonlinear equations of the reflector to calibrate the localization information. Thirdly, the report used the RDP(Ramer-Douglas-Peucker algorithm) algorithm to reduce the critical path points of the trajectory, which solves the problem of many path points in the traditional JPS algorithm. At the same time, used the optimal boundary NURBS curve algorithm to improve the robot trajectory quality. Finally, in large-scale simulation and physical environment, the navigation algorithm based on the reflector and the optimized smooth critical path trajectory algorithm was verified, proving the algorithm's effectiveness and accuracy in the complex domain.
Research on single image super-resolution algorithm based on region perception

In recent years, adversarial network-based research has made great progress in single-image super-resolution, In particular, the Enhanced Super-Resolution Generative adversarial networks (ESRGAN) can generate realistic and natural high-resolution images from low-resolution images. However, in the region with rich texture details, the super-resolution results generated by the existing algorithms are often unreal or inaccurate, and often produce artifacts. In order to improve the perception quality of detail texture region, residual paths are added to ESRGAN network structure to enhance feature extraction. To enhance the discriminator's discrimination ability for texture details, we propose a super-resolution training strategy based on region perception. To reduce artifacts, we analyze the source of artifacts and construct artifact loss according to the local statistical information of artifacts. Experiments show that the proposed super-resolution network based on regional awareness achieves better perception quality than the GAN-based SISR presented in recent years.
Hi-Speed Visual Servo Docking for Multicopter UAV based on Velocity Control Mode

This paper focuses on airborne recovery control of multicopter systems. System architecture and a new control strategy based on the velocity control mode are presented for the multicopter. This control method outputs the velocity control signal directly and does not necessitate re-tuning the inner loop controller. The multicopter tilt motion is also considered in the controller design. RTK GPS works together with an onboard RGB-D camera for accurate docking operations. Both simulation and field test results are presented to validate this method.
Proper Reuse of Features Extractor for Real-time Continual Learning

Real-time continual learning which could continually learn a series of computer vision tasks, has received more attention on robotic platforms and embedded systems with on-device training manner. However, most existing real-time continual learning systems need more training time when learning new visual perception tasks; the class imbalance issues are neglected amongst most real-time continual learning systems, which could degrade the generalization performance among past learned tasks, as well as the new tasks. To address these challenges above, we in this work propose to preserve and reuse the learned knowledge to achieve real-time continual learning. To be specific, when encountering a new visual perception task, we choose to freeze the learned backbone weights of all the past tasks, which could speedup the training process for new tasks. Moreover, we also store several activations volumes from some intermediate layer, which could further reduce the computational cost. For the improved perception performance, a focal loss is employed to guide the attention for poorly-identified sample categories and mitigate the class imbalance issue. Experimental results on popularly-used continual learning justify the efficiency and effectiveness of our proposed method.
Path Planning of Soccer Robot Based on Improved Heuristic RRT* Algorithm

For problems solving the soccer robot path planning in the RoboCup standard platform league, an improved algorithm based on Informed Rapidly-exploring random tree* (RRT*) is proposed. A series of improvements have been made to solve the poor purpose and low efficiency in searching for the initial solution in the Informed RRT* algorithm. Firstly, by estimating the initial states and adopting proper inflation strategy, the heuristic sampling subset is constructed in advance to increase the time proportion of constrained sampling. Secondly, the synchronous growth of bidirectional tree is used to replace the traditional alternating growth to simplify the planning task of single random tree, and a special stage is introduced to accelerate the connection. Finally, we adopt the hybrid cache mechanism to reuse the existing nodes, so as to reduce the calculation scale. A series of experimental comparisons show that the improved algorithm has better real-time performance and efficiency.
Trajectory Planning for the Wheeled Mobile Robot Considering Diamond-shaped Velocity Constraints

The trajectory planning of a two-wheeled differential mobile robot with diamond-shaped velocity constraints is studied in this paper. First, the kinematic model and the diamond-shaped velocity constraints of the mobile robot is given. Then, the artificial potential field (APF) functions are established for the target point and obstacles in the environment map. By solving a series of constrained optimization problems, the global trajectory planning can be achieved. Finally, we use the cubic spline method and the model predictive control (MPC) algorithm to further optimize the planned trajectory to make it smoother and ensure that the diamond-shaped speed constraints are met. Simulation results present the effectiveness of the proposed algorithm.
MSDR-Net: Multi-Scale Detail-Recovery Network for Single Image Deraining

Rain streaks vary in size, direction, and density, resulting in serious blurring and image quality degradation, which often directly affect the downstream visual tasks. At present, many end-to-end image removal networks have achieved good results, but image details are often lost during processing. Therefore, we propose a novel detail-recovery network to solve this problem. Unlike the existing works, we regard image rain removal and detail restoration as two different tasks simultaneously. Specifically, we use two encoder-decoder networks to extract rain streaks and detailed features and design different feature extraction blocks for two encoder-decoder networks. Due to the different receptive fields of feature layers at different scales, the information extracted at each scale is also different. The tasks of image rain removal and detail restoration are considered from the multi-scale feature level. To better respond to the image details and take full advantage of semantic information of multi-scale features, rain removal and image detail restoration are carried out at different scales. The proposed method has been validated on datasets to verify its effectiveness.
Multi-Robot Spatio-Temporal Path Planning With Robot Contour Constraints

Multi-robot spatio-temporal path planning with robot contour constraints is proposed to solve the problem of MAPF solvers' inability to solve real-world MAPF problems. The top level of the algorithm performs a best-first search in a conflict tree whose nodes are a solution to the given MAPF problem. The paper also proposes a spatio-temporal A* improved algorithm as a bottom layer algorithm to find a route for individual robots. The route planned for the robot consists of steps. Each step contains the spatial and temporal information of the robot. A graph containing constraints is set before running the algorithm. The experiment is done for the algorithm with the conflict-based search (CBS) algorithm in simulation and real-world scenarios. The results show that the success rate and the correct rate of the algorithm are higher than the CBS algorithm, which proves the applicability and effectiveness of this algorithm in large-scale multi-robot scenarios in the real world.
Obstacle-aware MDNets for Sampling Polytope Trees in Multi-robot Formation Path Planning

We present obstacle-aware MDNets (OMDNets) for path planning of multi-robot formation. In contrast to the RRT tree, each node of the OMDNets tree represents a feasible region for one or more formations. Moreover, each tree edge connects two feasible region centers that share at least one configuration. The construction of this tree requires node sampling and connecting. However, the existing method based on random sampling is inefficient due to the lack of information about obstacles. Also, the formation is not considered while connecting nodes. Therefore, OMDNets accept obstacle information as input and output the next practical point that generates an obstacle-free convex polytope. Moreover, a rapid checker based on the Minkowski approach is proposed for determining if two feasible regions can be connected. After establishing the tree, tree search and nonlinear optimization identify the path to the objective with the lowest cost. OMDNets is compared to different algorithms in a variety of 2D situations. The results demonstrate that OMDNets is computationally efficient and capable of locating shorter feasible paths.
A Hierarchial Approach to Panoptic CAD Drawing Parsing System Based on Point and Symbol Location

Computer-aided design (CAD) drawing parsing is a fundamental step to both drawing design expansion and digital management in industrial field. Existing methods can only parse some areas of the complete drawing. In this paper, we propose the task of panoptic CAD drawing parsing, which requires locating all area symbols, composition symbols and their relations in the complete drawing. This task is challenged by locating tiny symbols in high-resolution images and the pixel-wise locating the composition symbols. Therefore, we propose a hierarchical panoptic CAD drawing parsing system based on deep learning. Concretely, we use a hierarchical approach to firstly parse the drawing layout regions and then locate tiny area symbols based on symbol location. Meanwhile, for the composition symbols, we propose the Symbol-as-Points (SaPs) algorithm to obtain pixelwise location. 800 industrial drawings are precisely annotated to verify the effectiveness of our approach. Extensive experimental results prove the effectiveness of the framework.
Medical ankle image denoising based on constrained least square filter

In this work, the constrained least square filter is introduced to remove the noise of medical ankle image. Firstly, the algorithm principle regarding to the constrained least square filter is studied. On this basis, a medical ankle image with Gaussian noise is processed based on constrained least square filter and low pass filter. Finally, the peak signal-to-noise ratio, PSNR, is introduced to quantitatively estimate the post-processing medical ankle image quality. The obtained results show that the PSNR to the post-processing image obtained by constrained least square filter is larger than that obtained by low pass filter, which validates that the constrained least square filter can remove the medical ankle image noise effectively.
Research on Power Quality Signal Reconstruction Method Based on Compressed Sensing

The phenomenon of power pollution is becoming more and more seriously, on account of the theory of compressed sensing, it is of great significance to the research of power quality disturbance signal recognition. To solve the problem of the current power quality signal reconstruction algorithms are not adaptive and low reconstruction accuracy, the sparse adaptive algorithm is proposed to apply to the power quality signal reconstruction. DFT basis is selected as the sparse basis, observation matrix is designed as binary block diagonal matrix and SACoSaMP algorithm is proposed to reconstruct the power quality signal, which uses recursive to dynamically adjust the real sparsity of the approximation signal through the change of residual. The experimental results show that, the power quality signals are reconstructed using the proposed algorithm, the SNR of all the reconstructed signals are higher than 32 dB, ERP are over 99.7%, and MSE are under 2.5%. The indexes of the proposed algorithm are obviously superior to the existing ROMP algorithm, which further verifies the superiority and applicability of the proposed algorithm.
YOLO-CFM: Improved YOLOv5 for Vehicle Detection in Drone-captured Infrared Images

Drone-based vehicle detection is a significant task in aerial images. It helps the city empower traffic management and disaster rescue. When drones navigate in different altitude, the objects scale change violently. Moreover, the objects are hard to distinguish in the low light conditions, which leads to great challenge of object detection. To tackle two issues mentioned above, we propose YOLO-CFM based on YOLOv5. First, we add one more prediction head to detect different-scale objects. Then, we propose a Context Fuse Module to fuse features with different global context information and receptive fields effectively. We evaluate the proposed YOLO-CFM on DroneVehicle dataset. Experimental results demonstrate that YOLO-CFM improves about 5%, which is compared with baseline model (YOLOv5)
Improved QEM simplification algorithm based on local area feature information constraint

To address the issue that the traditional Quadric Error Metrics (QEM) simplification algorithm cannot effectively maintain the two crucial visual features of model details and edges, this paper improved the algorithm and proposed a simplification algorithm based on the information constraint of model local area features. The algorithm considered the changes in the average area of the neighborhood grid, the bending degree of the region, and the quality factor of the grid before and after grid simplification, and the amount of information from these changes is combined with the quadratic error measure to form a composite simplification error value. A simpler detection scheme is also given based on the characteristics of the model boundaries and sharp feature areas. The detection results are used as one of the conditions for simplification to avoid oversimplification of the model detail feature areas and protection of the model edges. The experimental findings demonstrate that, compared to the QEM simplification algorithm, this algorithm successfully suppressed the rise in simplification error while retaining model detail characteristics, improving the quality of the simplified model mesh.
PSO-ELM Optimization Algorithm for Gastroscopic Image Classification

In medical image diagnosis, the effectiveness of pattern recognition methods is an important factor in the accurate diagnosis and evaluation of diseases. In recent years, image acquisition has been significantly improved, with devices acquiring data at faster rates and higher resolutions, but the lack of an effective image diagnosis method for gastric diseases will not meet the requirements of clinical applications if its diagnostic accuracy is not high. Therefore, this paper proposes to use particle swarm optimization to improve ELM to achieve effective recognition of gastroscopic images and provide an effective intelligent diagnosis method for medical disease diagnosis, and PSO-ELM obtains better classification performance in the experimental results of this paper. The classification accuracy was improved from 85% to 95%, the precision rate had 86.67% to 95.83%, providing an intelligent method to effectively improve the diagnosis of gastric diseases.
A Survey on Information Retrieval Method for Knowledge Graph Complex Question Answering

Knowledge graph question answering (KGQA) uses structured knowledge in the knowledge graph (KG) to answer natural language questions. Currently, simple question answering has been well solved, but the performance of complex question answering needs to be improved. In this survey, we first present the background knowledge about complex question answering based on KG. Then the information retrieval-based (IR-based) method is introduced. We describe each procedure in the IR-based method in detail and investigate the related tools. Finally, the work is summarized and future research is envisioned based on the problems of current the IR-based method directions. With this survey, we aim to provide novices in the field of KGQA with an entry point to a suitable the IR-based method, where each step of the method can be understood.
Event-triggered sequential fusion filter for multi-rate multi-sensor systems with correlated noises

This paper presents an event-triggered sequential fusion (ETSF) estimator for asynchronous multi-rate multi-sensor systems (MMSs) with correlated noises. The system state updates uniformly and different sensors uniformly sample observation data with the positive integer multiples of the state update period. Moreover, the observation noises are correlated with each other and are also correlated with the system noise at the same instant. Furthermore, an innovation-based event-triggered mechanism is adopted to reduce the energy consumption during network communication from the sensors to the estimator. First, the MMS is transformed into the single-rate system at state update points. Then, based on the arrival order of the measurement data from different sensor at each state update points, the optimal ETSF filter is proposed. Finally, the effectiveness of the proposed algorithm is verified by a simulation example.
Safe Exploration of Reinforcement Learning with Data-Driven Control Barrier Function

Reinforcement learning relies on exploration and exploitation to find optimal policies. However, unconstrained exploration might lead to unsafe actions that jeopardize the system safety. To address this issue, this work presents a RL-based framework that integrates model-based CBF to ensure safe exploration during learning. Rather than synthesizing CBF by hand for complex dynamic systems, we exploit data-driven methods to learn CBFs from collected demonstrations of safe and desirable behavior. Unlike prior works that restrict on off-line collected expert demonstrations to train CBF, the CBF in this work is learned not only from preliminary expert demonstrations, but also from the on-line generated data at runtime, resulting in improved adaptation to complex environments. Numerical simulations and physical experiments using Crazyflie quadrotors are carried out to demonstrate the effectiveness of the developed safe RL framework. The experiment video is available at https://youtu.be/uscl-BQsLRo.
Medical Small-scale Transportation Object Detection Algorithm Based on Multimodal Data Fusion

The emergence of the Covid-19 pandemic has greatly impact transportation, and unmanned transportation has been widely used in medical. The average precision of object detection as an important part in unmanned medical transportation. Object detection mainly relies on sensors of vehicles to obtain information about the surrounding obstacles like camera and LIDAR. In this paper, we introduce a new fusion way to fuse data from different modalities, as 2D and 3D object detection encouraging performance, they are typically based on a single modality and are unable to leverage information from other modalities. We leverage the geometric semantic consistency of 2D and 3D detection to obtain more accurate fusion results, and address the weaknesses of IoU in fusion network by using a generalized version as both a new loss and a new metric. The experimental evaluation on the challenging KITTI object detection benchmark, shows significant improvements in average precision, especially at bird’s eye view metrics, which shows the feasibility and applicability of the network.
Overlapping cell segmentation based on improved maximization of interclass varia

The phenomenon of overlapping cells in red blood cell images affects the diagnosis and prevention of diseases. Therefore, an overlapping cell segmentation based on the improved Otsu(Maximization of interclass varia) algorithm is proposed. First, the red cell image is preprocessed, and then the improved Otsu algorithm is used to segment the red cell image to distinguish the red cell region from the background region. Then, based on the feature that the red blood cell image is approximate to a circle, the radius of the red blood cell is obtained by particle size measurement, the contour of the red blood cell is obtained by chain code, and the geometric relationship is combined to obtain the center of the red blood cell. Finally, MSF is used to measure the effect of red blood cell segmentation, and several experiments are carried out to verify the reliability of this method.
PROFESSIONAL EDUCATION OF THE REGION — RESOURCE OF PERSONNEL SUPPORT FOR INDUSTRIAL GROWTH

The article contains the results of the analysis of the needs of enterprises of the Republic of Karelia in the training of personnel with different levels of education. The author has compiled a matrix of correspondence between the structure of employers» demand for skilled workers and employees and the supply of professional educational organizations in the region. The article discusses the main regulatory legal acts in the field of development of the education system and youth support in the region, reflecting the most relevant areas of interaction between authorities, business representatives and the educational environment of Karelia on personnel training. The main achievements of the region in the implementation of current regional projects on targeted training programs, the organization of career guidance work with young people, the employment of graduates and the use of modern interactive forms of education are presented. Special attention should be paid here to the introduction of practice-oriented training and passing demonstration exams at the leading industrial enterprises of Karelia, as well as retraining and advanced training of employees of enterprises according to programs that meet Worldskills standards. The author shows the infrastructure of the personnel training system of the republic and substantiates the importance of further development at the regional level of centers of advanced professional training and advanced training of personnel for the industrial sectors of the economy of the region.
Algorithm vs Processing Manipulation to Scale Genetic Programming to Big Data Mining

In the era of petabyte, robust machine learning tools are needed to cope with the volume and high dimensionality of data to min. Evolutionary Algorithms (EA), such as Genetic Programming (GP), are powerful machine learning techniques with great potential to deal with big data challenges. To better exploit their capacities, additional manipulations can help the EA to alleviate the computation cost and then better look insight the large data sets. This chapter summarizes some solutions and trends to address difficulties when training EA/GP on big data sets and proposes a taxonomy to classify these solutions on three categories: Processing manipulation, algorithm manipulation and data manipulation. Two approaches are then presented and discussed. The first one, from the processing manipulation category, parallelizes GP over Spark. The second one, from the algorithm manipulation category, extends GP with active learning using dynamic and adaptive sampling. For each approach, some guidelines of implementation into the GP loop over an EA Python framework are given. A combination of the two approaches is also presented. The efficiency of these solutions is then discussed according to some published experimental studies.