Reviewerly/exHarmony huggingface dataset:
An Ensemble Broad Learning Scheme for Short-Term Load Forecasting

The management of electricity systems benefits considerably from short-term load forecasting. Although various superior short-term prediction algorithms have been investigated, some problems are still encountered in practical applications: (1) When dealing with data containing noise, there are unpredictable effects on short-term load forecasting. (2) Current deep learning models are time-consuming. In this study, we suggest an ensemble kernel broad learning system based on residuals to resolve the aforementioned issues. Specifically, we merge the broad learning system with kernel learning, which helps obtain stable features to improve the performance and avoid getting into the non-optimal problem. In addition, to further improve the prediction accuracy, we fuse multiple base learners to obtain an optimal prediction model from the residuals of the loss function. The experimental findings exhibit the efficacy and efficiency of our suggested technique.
A Trajectory Tracking Algorithm Based on Interior Point Method for A Class of Mobile Manipulators

At present, due to the high redundancy and strong coupling characteristics of mobile manipulator systems, there are few and in-depth studies on coordinated motion planning. Aiming at the above problems, this paper proposes a coordinated control algorithm that can track the desired trajectory of the end-effector based on a class of 5-DOF mobile manipulators. The algorithm takes the power consumption and trajectory stability of the entire system as the optimization objectives, and sets the function of the selected optimization objective on the desired trajectory, and performs optimization based on the interior point method of convex optimization to converge to the optimal solution faster. Finally, the system is simulated to verify the correctness and effectiveness of the control algorithm.
Robot trajectory planning based on improved second-order oscillatory particle swarm algorithm

This study proposes a trajectory planning technique for robots using 3-5-3 polynomial interpolation that is based on an enhanced second-order oscillatory particle swarm algorithm with kinematic constraints like as velocity, acceleration, and jerk. A kinematic analysis of a six-axis industrial robot is carried out, and for trajectory planning, a 3-5-3 polynomial interpolation function is developed. For the issues where the conventional particle swarm technique is prone to local search and local optimality, the improved second-order oscillatory particle swarm approach is proposed. The technique uses time-jerk as the search space to find the best robot trajectory. To guarantee that the robot runs as quickly and smoothly as feasible, any optimization objective that does not satisfy the kinematic restrictions is removed. Trajectory planning in MATLAB for a six-axis industrial robot in joint space demonstrates the algorithm's efficacy under kinematic constraints.
ActionFormer: Pose-based Action Recognition via Transformer and CNN

Recently, human pose estimation and human skeleton capture system developed rapidly and raised more and more attention to pose-based action recognition. Deep learning algorithms like CNN and RNN have been applied to many tasks for the past few years. Although they have showed promising prospects, these methods lack the ability to take full advantage of spatio-temporal relationship information between the skeletons in the behavior explicitly, and the accuracy of action recognition is not easy to improve. We put forward a new and effective CNN-Transformer model able to extract spatial-temporal information for human pose-based action recognition to address these problems. Firstly, we extract the pose features from the original video. Secondly, the pose features are processed by action modeling module. Then, we feed the spatio-temporal information of the skeleton points generated by the modeling into CNN and Transformer modules respectively, in the form of relative position and relative speed. Finally, the output information obtained from the two modules is fused. What is more, we compare the model with the previous advanced methods and carry out comparative experiments on SUB-JHMDB, SYSU-3D and NTU RGB+D datasets. It is obviously that the model significantly improve the action recognition performance through the experimental results.
Research on intelligent methods in virtual reality games

Introduction of virtual reality technology enriches the expression of traditional games and greatly improves the sense of substitution and interactive experience of users. Realization of virtual reality is inseparable from artificial intelligence technology. In order to improve immersion and interactivity of virtual reality games, it is necessary to propose more intelligent solutions to existing technologies. This paper aims to discuss some intelligent methods of virtual reality games. Firstly, it discusses artificial intelligence means to enhance the virtual reality experience, which can improve the tracking and force / tactile interaction system of existing virtual reality games from software and hardware aspects; Then it proposes the concept of human-machine hybrid enhanced intelligence: by introducing human cognitive model to improve perception and decision-making ability of virtual reality games, so as to make interaction of virtual reality games more friendly and more real.
Research on Intelligent Recommendation Method for Health Managementer

Aiming at the problem that the application of intelligent technology in the elderly is congenitally inadequate, this paper studies the method of forming intelligent recommendation for the elderly’s health management services, and proposes an intelligent recommendation system model for health management. The model is divided into data layer and application layer. The data layer uses knowledge mapping technology to build a knowledge mapping database in the health field, which is stored through Neo4j to provide a data base for the upper application. The application layer forms health intelligent recommendations for users through querying the database. Traditional intelligent recommendation is based on the interactive information between users and items to speculate user preferences, thus forming recommendations. It is consumer oriented intelligent recommendation; The health management intelligent recommendation proposed in this paper is based on health management knowledge, speculates the health needs of users rather than preferences, and thus forms recommendations for users. It is an intelligent recommendation for the served.
A camera calibration method suitable for complex illumination conditions

Images in weak or strong light usually have problems of uneven illumination and low contrast. Obtaining high-quality images is an important guarantee for good camera calibration results. In order to solve the problem of low camera calibration accuracy under complex lighting conditions, an image enhancement method based on Retinex under complex lighting conditions is proposed, and camera calibration is studied. Firstly, the color enhancement and color constancy of the image are realized based on the MSR algorithm. Then, the calibration plate image is processed by Gaussian homomorphic filtering to improve the image contrast and other indicators, and the final calibration plate image is obtained through image segmentation and bilateral filtering combined with Otsu algorithm. Finally, based on Zhang Zhengyou’s calibration method, comparative experiments are carried out under weak illumination, uneven illumination and strong illumination conditions. The experimental results show that the algorithm enhances the details of the calibration plate image under complex lighting conditions and improves the accuracy of camera calibration under complex lighting conditions.
Anomaly detection of gas flow meter based on improved GAN

As one of the core equipment in the gas industry, gas flow meter guides the operation and maintenance of gas companies and reflects the gas consumption habits of downstream users. Therefore, the anomaly detection of gas flow meter has important theoretical value and practical significance. However, the sensors of the gas flow meter are coupled with each other, and the collected data has the characteristics of difficult to collect negative samples. For the solution of the above challenges, an approach to unsupervised anomaly detection based on generative adversarial networks (GAN) is introduced in this paper. Firstly, after the flow meter signal is processed with the data screening, the Savitzky Golay (S-G) filter can filter out the noise from the raw data. The information between channels is captured by the feature attention module for improving the accuracy of the potential representation of samples, in addition, residual blocks are used to prevent the network degradation phenomenon in the deep network. Finally, the proposed model was demonstrated to be valid by using the actual user gas flow meter data. Compared with the current popular depth learning methods, the experimental results reveal that the proposed method can be very accurate in terms of ACC and F1 scores.
Correlation Filtering Object Tracking Algorithm Based on Motion Estimation

In order to solve the bounding box deviation while the object moves rapidly, which will cause the loss of the object, an improved Kernelized Correlation Filter object tracking algorithm is proposed based on motion estimation. The block-matching algorithm is used to estimate the motion of the tracking object in the adjacent frames of the video sequence, and the estimated results are quantified to judge the severity of the object’s motion. Then the size of the search box is adaptively adjusted. Two data sets of both OTB100 and OTB50 are selected for the experiments. The experimental results show that the improved algorithm can effectively solve the problem of object loss caused by rapid object movement and achieve long-term stable tracking of objects.
An Improved Remora Optimization Algorithm with Lens Opposition-Based Learning for Global Optimization

The remora optimization algorithm (ROA) is one of the newly proposed swarm behavior-based algorithms. During the implementation of ROA, each search agent updates its position with the host, which leads to the algorithm difficult to converge and solve complex engineering problems. To alleviate these situations, Lens Opposition-Based Learning (LOBL) is added in this paper to optimize the performance of ROA when solving complex problems. The improved algorithm called IROA. LOBL is inspired by the imaging principle of the optical lens. Moreover, the LOBL strategy can improve the probability of the traditional ROA escaping from the local optimal solution. To bear out that the proposed IROA is functional, this paper selects 13 benchmark functions to prove the superiority of the improvement. By comparing IROA with five meta-heuristics, the experiment results illustrate that the IROA is more effective in working out benchmark optimization problems. Additionally, the performance of IROA in car crashworthiness design also reveal that IROA can get the optimal solution in solving practical optimization problems. Generally speaking, all the above experimental results validate the effectiveness of the proposed algorithm.
Simulation of synchronization control for ships in waves based on hydrodynamic forces

In the course of maneuvering between two ships, a certain distance must be kept between the leading ship and the following ship to avoid collision. In order to simulate the synchronous maneuvering control between two ships, the hydrodynamic model test and modeling simulation of a single ship were carried out at first. Then, based on the interaction force between two ships, the synchronous control simulation of two ships under wave conditions was completed. Finally, the effectiveness of the synchronous control method for two ships in waves was verified by simulation. Hydrodynamic interaction and wave force are the main factors affecting the navigation control and safety of the two ships.
Mapless Navigation Based on VDAS-PPO Deep Reinforcement Learning

Low sample utilization is a problem for the continuous action space’s Proximal Policy Optimization (PPO) algorithm. This paper proposes a Proximal Policy Optimization (VDAS-PPO) based on Velocity-Directed Action Selection. Firstly, the multi-information fusion reinforcement learning network is designed for mapless navigation to well utilize sample experience and information representation in the current state by fusing target location information, velocity value information, and lidar observation value information. Moreover, during the decision-making process, a novel reward function is proposed to optimize the action decision by designing a speed variation factor. The sample utilization rate is significantly increased by the VDAS-PPO algorithm, which also guarantees that the agent can learn quickly and effectively when training the network model. Three selected scenarios are built up on the simulation platform Gazebo to test the effectiveness of the proposed VDAS-PPO algorithm. The training episodes of the algorithm in this paper are decreased by 5–6 times compared to the PPO and Deep Q Network (DQN) algorithms, and the SPL value is also enhanced.
Kernel correlation filtering algorithm based on fractional Fourier transform

Robust tracking is an important and challenging task in the field of computer vision. Kernel correlation filtering is one of the most popular tracking algorithms. Diversity and complexity of tracking objects usually lead to degradation of its tracking performance. To improve the tracking performance, we propose a kernel correlation filtering based on fractional Fourier transform. We replace Fourier transform by fractional Fourier transform. The Peak-to-Sidelobe Ratio of the tracking object in the response map becomes higher, which benefits the tracking performance. Extensive experiments on the dataset OTB100 show the advantage of our proposed method.
MB-CapsNet: An Efficient Fusion of MobileNetV3 and Matrix Capsule Networks

The capsule network constructs the relationship between the part and the whole in an image through feature coding, which shows excellent performance in the image classification. However, the original capsule network is weak in extracting useful feature information, because it tends to explain all the information in a graph and the advantages of the capsule network cannot be reflected in the face of complex background datasets. By using MobileNetV3 as the backbone to replace the simple primary convolutional network layer, an improved capsule network named MB-CapsNet is proposed in this paper. The advanced network reducing the interference of invalid features to focus on effective features. At the same time, a variety of parameter sets are employed and extensive experiments are proceeded on four kinds of datasets. After that, an optimal network configuration is obtained which is suitable for different datasets. The experimental results illustrate that the advanced MB-CapsNet is more effectively than the original capsule network in the feature extraction, showing its advantages and potential in complex datasets.
Posture angle test based on RGB-D neural network for ice and snow sports elbow pads

A target recognition method based on RGB-D network and calculated the posture angle by measuring the markers on the prosthesis arm to address the problem of real-time measurement of prosthetic posture angle for the analysis of the motion effectiveness of existing ice and snow sports elbow pads is proposed. Firstly, on the basis of YOLOv5 network, a dual-channel RGB-D network model is designed to recognize the posture of wearing the elbow pads prosthesis arm and analyze the motion effectiveness of the ice and snow sports elbow pads. Secondly, recognition experiments are conducted under different lighting conditions, and compared with YOLO V5, Faster RCNN target detection model, the designed network recognized 8.5% and 7.2% improvement in mPA, precision is improved by 5.9% and 3.3% respectively. Finally, after applied to the pose angle analysis of the ice and snow sports elbow pads, it is proved that the dual-channel RGB-D network can effectively improve the accuracy, speed and robustness of target recognition, and can be used for real-time target detection. The designed network model can be applied to the posture angle test in the ice and snow sports elbow pads movement effectiveness test to assist in analyzing the ice and snow sports elbow pads movement effectiveness.
Panoramic Stereo Matching Network Based on Bi-Projection Fusion

Stereo Matching from panoramic images is becoming a hot research topic because panoramas have an entire field of view and provide a complete semantic scene. However, due to the nonlinear epipolar constraint, estimating depth from an equirectangular projected panoramic stereo is suboptimal. The cubemap projection is another representation, which is undistorted and has a more straightforward epipolar constraint but introduces discontinuities at the boundaries of the cube. Considering that the two projections are complementary, we proposed an end-to-end trainable network based on bi- projection fusion for stereo depth estimation using 360° images. To resist the interference of the distortion in the equirectangular projection, we constructed cost volumes utilizing epipolar constraints in equirectangular and cubemap projections, respectively. In addition, we designed two fusion modules for the fusion scheme, one of which has high computational accuracy and the other is faster. Numerous experiments and ablation studies are given to validate our approach against the current algorithm.
Efficient Video Stitching for an Eagle-Eye-Based Vision System

Computer vision has always been one of the research hotspots in the field of artificial intelligence. Inspired by the characteristics of eagle eye, this paper first designs a bionic visual imaging module that contains two wide-angle cameras and one telephoto camera. Then, video stitching and video fusion are accomplished by exploring the fact that relative poses of these three cameras keep constant. Results of extensive experiments show that the eagle-eye-based vision system can acquire large field of view and local high-resolution images simultaneously, which lays a good foundation for subsequent vision tasks.
CO<sup>2</sup>Det: Anchor-free based Convex-hull Generation Network for Oriented Object Detection

Nowadays, arbitrary oriented object detection has achieved considerable progress in the field of remote sensing image interpretation. However, there remains challenges of label assignment and loss discontinuity. To put the axe in the helve, we propose an anchor-free Convex-hull Oriented Object Detector (CO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Det). In CO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Det, the convex-hull feature expression(CFE) module and Kullback-Leibler Divergence(KLD) loss are proposed, based on the 135° long edge prediction box definition. In the CFE, a convex-hull set is constructed for each object so strengthen the connection of non-axis aligned feature from background pixels or adjacent objects. To maintain the loss continuity and scale invariance, a general rotation detection loss KLD is devised that transform the convex-hull parameters into a 2-D Gaussian distribution. KLD is applied to release mutual constraints between parameters so that adjust the direction optimization strategy adaptively. The DOTA and HRSC2016 public datasets are chosen to train our model and achieved the state-of-the-art performance.
Hand Detection and Gesture Classification based on Transformer

Hand detection and gesture recognition are an important branch of artificial intelligence, which are widely used in daily life, such as interaction between human and machines, communication using signals, virtual reality and so on. In order to realize gesture recognition in unconstrained situations, we propose a novel end-to-end model based on Transformer, which can detect multiple hands in a picture. Our network can be roughly divided into the following three stages. In the first stage, we will extract feature maps of different layers by convolutional network from monocular RGB images. In the second stage, features are sent into the codec module based on Transformer, and hand detection is further carried out through multi-layer perceptrons with the decoding vectors as the input. In the third stage, we combine the attention weight maps obtained in the second step with the features of different layers in the first step to complete the gesture recognition task. From the experiment results, our network accurately identifies the positions of multiple hands in the picture and correctly recognizes hand gestures.
Value-Decomposition Multi-Agent Proximal Policy Optimization

We explore the policy-based approach for the newly well-liked centralized training and decentralized execution (CTDE) mechanism’s multi-agent reinforcement learning (MARL) job. Multi-agent proximal policy optimization (MAPPO) achieved optimal effect in multiple multi-agent cooperative tasks. However, it performs poorly in more complex multi-agent cooperative tasks because it does not solve the problem of credit assignment. To deal with this issue, we combine the method of value decomposition in value-based reinforcement learning with policy-based MAPPO, and propose a new actor-critic algorithm, namely value-decomposition multi-agent proximal policy optimization (VDPPO). VDPPO uses value decomposition to provide different rewards for agents to distinguish their contributions and train the policy network using the advantage function. To determine the effectiveness of our algorithm, we modify the multi-agent particle environments (MPE) and carry out experiments on it. The experimental findings demonstrate that our suggested method is more advantageous compared to several baseline algorithms and is suitable for more complex multi-agent cooperative tasks.
Distributed PSO for Rescue Task Allocation with Multi-Type Multi-Task

Currently, task allocation evolutionary algorithm that use a centralized type for communication is a popular issue, while such scenario is very rare in practical. Given that, this paper presented a PSO-based distributed method to solve the problem of rescue task allocation with multi-task by multi-type robots. Existing distributed algorithms, such as, CBBA, PI, PI-softmax, PI-e-greedy are employed to generate solutions for solving the problems, however, there still exist room to improve. Due to task allocation problem is a NP-hard problem, and PSO is employed and modified so as to solve the above problem in a distributed manner. Firstly, the problem is descripted and the mathematical model is established, the average mission time for each task is set as the objective function, in addition, the priority sequence computation method proposed in [21] is also employed in this paper; secondly, PSO is modified to be a distributed algorithm, and to better the result, multiple sub-swarms is illustrated corresponding to multiple types of tasks or robots, and some new operations, including: the initial phase, the local and global best solution updates outside the sub swarms and among the sub swarms, the velocity and position update method and so on, are presented. The existing scenarios and other complex scenarios are solved to verify the proposed method, and then, the corresponding results illustrate that the proposed distributed PSO algorithm not only can handle the problem but also can gain the result within an acceptable time in such a rescue scenario.