Reviewerly/exHarmony huggingface dataset:
An Adaptive Object Tracking Algorithm with Multi-Features Based on Correlation Filtering

Aiming at the problem that visual object tracking algorithms based on correlation filtering using only Histograms of Oriented Gradients (HOG) feature has unsatisfactory tracking performance, an adaptive object tracking algorithm with multi-features is proposed based on background-aware correlation filters framework. An adaptive fusion module of HOG feature response and color feature response is constructed to improve the robustness of the algorithm in different tracking scenarios. A novel feature response evaluation index named significance of main peak is designed to enhance the accuracy of feature response discrimination and fusion. A model adaptive update module is presented to reduce the risk of model drifts and improve tracking performance. Through evaluating on OTB2015 dataset, experimental results show that the tracking algorithm has excellent comprehensive performance and can achieve more accurate real-time object tracking.
Intake Gesture Detection With IMU Sensor in Free-Living Environments: The Effects of Measuring Two-Hand Intake and Down-Sampling

Food intake monitoring plays an important role in personal dietary systems. Numerous approaches have been proposed to automatically detect eating gestures using various sensors and machine learning. However, existing eating gesture detection approaches mainly focus on meal sessions. Such a task is still challenging in free-living environments due to longer monitoring duration and more non-feeding activities. This paper proposes a wearable Inertial Measurement Unit (IMU) based method to detect eating and drinking gestures in free-living environments. Two important factors that impede intake gesture detection in free-living environments are addressed: 1) how to handle IMU data from two hands, and 2) what is the impact of downsampling sensor data on performance. To integrate two-hand data, we propose a solution that combines hand mirroring and temporal concatenation techniques. The multi-stage temporal convolutional network (MS-TCN) is applied to effectively recognise intake gestures. A dataset contains 12 subjects with 67.5 h data is collected for validation. Moreover, IMU data with different sampling frequencies are processed to test performance. Validated by Leave-One-Subject-Out (LOSO) method, our approach (with 16 Hz sampling frequency) achieves a segmental F1-score of 0.826 and 0.893 for recognizing eating and drinking gestures, respectively. Results show that the proposed solution outperforms existing two-hand data combination approaches. Moreover, in our case, a higher sampling frequency does not always mean better performance.
Cooperative obstacle avoidance method of multiple unmanned underwater vehicles based on improved artificial potential field method

Aiming at the obstacle avoidance problem of multiple unmanned underwater vehicles (UUV) formation in different obstacle avoidance scenarios, the collaborative collision avoidance algorithm of multiple UUV formation was studied. In terms of obstacle avoidance algorithm, an algorithm combining artificial potential field method and consistent formation control algorithm is proposed. A variety of obstacle scenes are set by using the information of forward looking sonar, and different weights are set for the fusion of control instructions of the two algorithms. The problems of formation recovery time and formation error caused by formation "grouping" in the course of UUV obstacle avoidance are solved. Finally, in the underwater simulation platform, the effectiveness and advantages of the collaborative obstacle avoidance algorithm in multiple scenes are verified by experiments.
Photovoltaic Power Prediction of Meteorological Based on Data Drive and Mechanism Model

The proportion of photovoltaics (PV) in the grid has increased significantly with the extensive use of renewable energy. However, natural factors such as weather have a great influence on the output power of PV. Affected by the randomness and volatility of PV output power, there are unpredictable power disturbances, which can easily cause a mismatch between PV output and load demand, resulting in bus voltage fluctuations, thereby affecting the safety and stability of the system. Traditional PV power prediction models usually directly uses the data characteristics such as horizontal solar radiation flux and temperature in the meteorological forecast, ignoring the effect of solar angle on power prediction. In order to further improve the accuracy of PV power prediction, a prediction method based on the meteorological data driven and mechanism integration is proposed. Firstly, the influence mechanism of solar angle on power prediction is analyzed using the solar radiation bevel conversion principle, and the solar angle solving model is established. Secondly, the meteorological forecast data is expanded on the basis of the solar angle mechanism, the point forecast and the trend forecast models are established, and the prediction results are fused with the error index. Finally, through the test of different prediction time standards for 1 day, 3 days, and 7 days of the data of multiple real light fields, the effectiveness of the method proposed in this article was verified.
Cooperative Hunting Method of Unmanned Surface Vehicle based on Attention Mechanism

The hunting of dynamic targets by multi unmanned surface vehicle(USV) is an important problem in USV swarm operations. In this paper, aiming at the dynamic target oriented swarm hunting problem, by analyzing the shortcomings of the hunting mechanism based on MADDPG algorithm, we introduce the attention mechanism into the hunting process, and then design the cooperative hunting strategy based on the attention mechanism(Att-MADDPG), updating the corresponding hunting algorithm. Firstly, the attention module is added to critic network to process the information of all USVs according to different attention weights. Secondly, the attention module is added to actor network to promote other USVs to carry out cooperative hunting. Finally, establish obstacle-free and obstacle simulation environment according to the hunting task. The simulation results show that the training stability and timeliness of Att-MADDPG algorithm are significantly improved compared with MADDPG algorithm. After learning, the USV can cooperate to make the swarm emerge more intelligent behavior.
Underwater visual image enhancement method based on the fusion of optical-model methods and model-free methods

The ability of underwater robots to autonomously identify specific targets is fundamental for underwater robots to complete underwater tasks autonomously. However, the quality of underwater images decays with increasing underwater depth, resulting in low-contrast and high color distortion. To address this issue, an underwater image enhancement algorithm based on the fusion algorithms of optical-model methods and model-free methods is proposed in this work. The depth map features of a specific scene are first acquired, and the depth map is used as input to measure the attenuation and transmission maps. Subsequently, the images were optimized at the pixel level by improving the model-free based methods of background light estimation, improving the red channel prior, improving gamma correction, and image sharpening. For underwater images with light sources and white bodies, we use a new estimation algorithm based on global background illumination in the background light estimation part, which successfully solves the problem of uneven illumination caused by traditional processing. For the red channel prior, we proposed an adaptive gamma correction method. We connect image pixels with gamma values using the region mapping technique to improve the image quality without knowing the illumination information. Finally, a novel pyramid fusion technique is proposed to fuse the results of optical-model methods and model-free methods. In this paper, the UIQM and UCIQE are used as underwater image quality evaluation metrics. Experimental results show that the proposed method improves 2.23 and 1.311 in two metrics compared to the FUnlE-GAN algorithm, 0.34 and 1.331 compared to the Deep WaveNet algorithm, 0.45 and 1.241 compared to the Retinex-based algorithm, 0.78 and 1 compared to the Automatic red channel algorithm, 0.21 and 0.699 compared to the WCID algorithm, respectively.
Aerial Autonomous Docking Control Method of Quadrotor UAV

The unmanned aerial vehicles (UAV) can autonomously assemble in the air, so as to deal with different scenarios and mission requirements. However, on the problem of autonomous aerial docking of rotorcraft UAVs, there are few existing research and most of them focus on docking with hovering UAVs. This paper proposes a solution to the problem of docking with hovering or moving quadrotor. The whole docking process is divided into the approach phase, the tracking phase and the docking phase. In the tracking phase and the docking phase, guidance methods are proposed according to the phase targets correspondingly. A docking controller which can adaptively estimate the desired point velocity and external disturbance is used to control the quadrotor UAV to complete the docking. The final simulation results show that the proposed method can complete the autonomous docking with a moving quadrotor UAV under disturbance.
Two-person Interaction Recognition Algorithm Based on the Joint Points Attention Motion Features Map

The human interaction recognition based on joint point date and RGB video has broad application prospects. RGB video contains information such as the appearance and texture of the human body. The joint point data is the coordinate’s position of three-dimensional skeleton during the human body movement. The two features have good complementarity. However, the fusion methods currently studied mostly adopt the decision-level fusion method, leading to less change of the fusion form and failing to make a better use of the complementarity of the two data sources. In response to the above problems, a human interaction recognition algorithm is proposed by fusing joint point data and RGB video in the feature layer. The RGB video is used as the main identification information, and the joint point data is used as auxiliary identification information in the proposed method. First the joint points attention motion features map is constructed by analyzing the joint point data to obtain the main motion part, and paying attention to the corresponding position of the RGB motion feature. Then the VGG19 network is used to extract deep features of the joint points attention motion features map. Finally the Bi-LSTM is introduced to perform timing modeling to obtain the final recognition result. The method was tested on the international standard NTU RGB+D database. The test results show that the algorithm greatly improves the accuracy of similar actions recognition, which proves the feasibility of the algorithm. This method provides a novel solution based on the fusion of RGB video and joint point information. It is simple and easy to implement, and has good practicability.
Intelligent Prediction Modeling of Oxygen Content in Flue Gas for Municipal Solid Waste Incineration Process

To accurately predict the oxygen content in flue gas during the municipal solid waste incineration process, this paper uses mutual information, differential evolution algorithm and stochastic configuration network to select the features of the oxygen content in flue gas and predict the modeling. Firstly, the mutual information method is used to eliminate some irrelevant variables. Secondly, the differential evolution algorithm is combined with the stochastic configuration network to further eliminate redundant variables from the above selected feature variables, so as to determine the input variables for predicting the oxygen content in flue gas, and train the stochastic configuration network prediction model for the oxygen content in flue gas. Finally, the actual data sampled from a solid waste incineration plant in Beijing is used to test and verify. The results show that the hybrid feature selection method and prediction modeling method in this paper are effective and can accurately predict the oxygen content in flue gas during the solid waste incineration process.
On the basis of A<sup>*</sup> Algorithm and improved ant colony algorithm for intelligent robot path planning

A path planning model based on the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm and improved ant colony algorithm is proposed to solve the path optimization problem of handling robot sorting multiple goods at one time. Firstly, the plan of the warehouse and the location of the goods are converted into black and white grid maps by grid method, and then the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm is used to convert the Manhattan distance between all nodes into an undirected graph. Secondly, the pheromone updating principle of the traditional ant colony algorithm is optimized to artificially release additional pheromones on the optimal path to enhance the positive feedback effect, thus speeding up its convergence speed and obtaining the optimal solution. Finally, the performance of the algorithm is tested by simulation. The simulation results show that compared with the traditional ant colony algorithm, the improved ant colony algorithm has better convergence speed and higher efficiency under the condition of a large number of nodes.
A General Overview of Intelligent Sorting System Based on Machine Vision

With the development of computer-related technologies, robot technology and machine vision technology have also been greatly improved. The intelligent sorting system composed of traditional robots and machine vision can not only reduce production costs, but also improve the efficiency and accuracy of sorting operations. With these advantages, the machine sorting system is gradually replacing the manual sorting system. It develops in the direction of networking, automation, collaboration and intelligence, and has extremely broad development prospects. Therefore, this paper discusses and analyzes the composition, classification and application of the intelligent sorting system in detail, and expounds the key technologies. At the same time, the intelligent sorting system proposed in this paper is introduced. Finally, the current development of intelligent sorting system is summarized, and the future development trend is prospected.
Track planning model of USV based on multi-granularity pattern ant colony algorithm

Aiming at the problem of cooperative track planning with multiple USV, a path planning method based on multi-grain pattern ant colony algorithm was proposed. The cost function of USV collaborative track planning was redefined. Meanwhile, the ant colony algorithm was studied and improved, and the multi-grain mode ant colony algorithm was proposed. The correctness of the proposed multi-grain mode ant colony algorithm was proved by the simulation of TSP problem. The multi-grain pattern ant colony algorithm proposed in this paper is applied to track planning. When dynamic threats occur, the heuristic evaluation function is constructed by using the rolling optimization method to guide the ants to search direction. Simulation results show that the proposed method can effectively avoid sudden mobile threats.
HierNet: Hierarchical Transformer U-Shape Network for RGB-D Salient Object Detection

With the popularity of depth sensors, research on RGB-D salient object detection (SOD) is also thriving. However, given the limitations of the external environment and the sensor itself, depth information is often less credible. To meet this challenge, existing models often purify the depth information using complex convolution and pooling operations. This causes a large amount of useful information besides noise to be dropped as well, and multi-modality interaction chances between RGB and depth become less. Also, with the gradual loss of information, the hidden relationship of features between multi-level is thus ignored. To tackle the aforementioned problems, we propose a Hierarchical Transformer U-Shape Network (HierNet) that include three aspects: 1) With a simple structure, a depth calibration module provides faithful depth information with minimal loss of information, providing conditions for cross-modality cross-layer information interaction; 2) With multi-head attention, a set of global view-based transformer encoders are employed to find the potential coherence between RGB and depth modalities. With weight sharing, several transformer encoder sets comprise the hierarchical transformer embedding module to search long-range dependencies cross-level; 3) Considering the complementary features of U-shape network, we use dual-stream U-shape network as our backbone. Extensive fair experiments on four challenging datasets have demonstrated the outstanding performance of the proposed model compared to state-of-the-art models.
UAV target tracking algorithm based on kernel correlation filter

Three improvements are made to address the traditional kernel correlation filtering algorithm in terms of poor robustness of single features, unsound scale update and model update mechanism: first, by studying the focus of each feature, the directional gradient feature (HOG), color feature (CN) and edge feature (EDGE) are fused, and a feature with stronger discriminative ability can be obtained; second, the scale pyramid principle is used for scale changing targets for adaptive tracking; finally, a combination of average peak correlation energy (APCE) and maximum response is used to propose a template update method with adaptive learning rate, so that the algorithm has higher robustness in the face of occlusion and thus improves the tracking accuracy of targets. Finally, the algorithm comparison experiments are conducted on the UAV123 dataset. The results show that compared with the traditional algorithm, the improved algorithm has significantly enhanced target discrimination, scale perception and anti-interference ability, and can effectively cope with the common challenges in UAV tracking process.
Liquid Level Line Recognition Method Based on UNet Semantic Segmentation Network

In response to the problems of inapplicability, low accuracy and high cost of existing liquid level detection methods for filling systems, a method of liquid level line recognition based on UNet semantic segmentation was proposed. The liquid level line in the container can be accurately identified by photographing the liquid level image inside the container. Firstly, the liquid level image data set was created through digital image processing. Secondly, feature extraction based on UNet semantic segmentation model was carried out for the data set. At last, each pixel of the liquid level image was classified, and the liquid level of the image was predicted. The average intersection union ratio (MIoU) was 90.95%, and the average pixel accuracy (MPA) was 96.21%.The experimental results showed that the model has good generalization and prediction effect.
A synthetically photogrammetric system for precise contour parameters with bi-telecentric imaging

The traditional method is inefficient and expensive, and the traditional machine vision measurement method has issues with inadequate measurement accuracy and low stability. However, the contour distance measurement of the traditional polygonal workpiece plays an important role in ensuring its processing quality. In this research, a high-precision measuring method for polygonal workpiece contour distance based on photographic bi-telecentric lens imaging is created in order to achieve greater manufacturing quality. First, a bi-telecentric imaging method-based analytical modeling and overall structural design of the measuring system is completed; next, a more accurate approach for exact edge extraction of contour images is proposed. By performing quadratic function interpolation on the gradient modulus values of nearby three points in the gradient direction of candidate edge points, this method raises the connection degree of the edge point chain and yields more precise sub-pixel coordinates of edge points. Finally, the RANSAC algorithm improves the contour line. obtaining the contour distance using the multi-sample interactive point-line distance computation method. The average relative error of the measured contour distance obtained by this system is less than 0.007mm, and the repeated measurement accuracy is less than 0.2m, according to an experiment measuring the contour distance of a polygonal workpiece at various points in relation to the nominal value highly reliable measurement of the profile distance.
Multi-UUV task assignment strategy based on 0-1 integer planning

To solve the task assignment problem of multiple underwater unmanned vehicles, a 0-1 integer programming method for solving the task assignment problem is proposed based on an in-depth analysis of the task assignment characteristics of multiple underwater unmanned vehicles. According to the demand of task assignment, a non-standard form of task assignment model is established on the basis of standard integer programming which is more in line with reality; the constraints are formalized and specific constraint functions are obtained by arguing and analyzing the position and state information of the underwater unmanned vehicle; according to the actual demand of different working conditions, the objective functions are designed to meet the reality of each working condition. The experimental results show that the model can effectively and quickly solve the task assignment problem of multiple underwater unmanned vehicles.
Library Consortia and Networks

Library consortia, or networks, are groups of libraries bound together by certain criteria, be it type of library, geographic, or other more nuanced criteria such as governance and funding structure and sources. The goals of library consortia can vary greatly based on the membership; however, standard goals can include licensing and negotiation of electronic resources, shared print resources, shared technological infrastructure, and professional development. There are numerous consortia worldwide, the majority of which are in North America and Europe. Future concerns for consortia include the rising costs of resources and technology and funding for open-access materials.
An active contour model driven by K-means clustering for image segmentation

Active contour model (ACM) is one of the most often utilized approaches in the application of image segmentation owing to its excellent segmentation accuracy. However, common issues such as under-segmentation and over-segmentation may frequently take place by utilizing the majority of existing ACMs. To solve these issues, this study incorporates K-means (KM) clustering algorithm into the energy function, which can accurately process different kinds of images. In contrast with recently developed ACMs, the proposed KM model obtains better segmentation results (The CPU elapsed time T, iteration number N, and DSC). In addition, compared with fuzzy c-means (FCM) clustering algorithm, this model exhibits a relatively faster segmentation speed.
A Novel Cross-Device Target Handover Method based on Affine Transformation and Correlation Filter

In this paper, we propose a novel template matching method, which achieves target handover between the video monitoring system and security mobile robot. First, a target coordinate transformation scheme across devices is proposed to get the target pixel range in robot perspective. Second, correlation filters utilized to complete target matching process. Finally, an improved target handover method based on affine transformation and correlation filter is proposed, which achieves higher accuracy. Compared with traditional template matching methods and deep learning methods, our proposed method achieves promising performance in both speed and accuracy.