Reviewerly/exHarmony huggingface dataset:
Collaborative A<sup>*</sup>-Market-based Intelligent Planning Algorithm for Multi-Spraying Robots

This paper addresses the cooperative path planning problem of multi-spraying robots with rectangular spraying areas, where obstacles are the rectangular spraying areas that are once completed. A novel collaborative A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> -market-based planning algorithm is designed to achieve the cooperative path planning with obstacle avoidance. The optimal performance of the robot utilization, the short time and the low path cost are under consideration and achieved by the proposed A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> -market planning algorithm. Three scenarios with the fixed starting points of each rectangular spraying area (that is, the upper left vertex, the upper right vertex, the lower left vertex and the lower right vertex) and without fixed starting points are given in simulation. It is indicated that the average time cost and the total distance cost in the case without fixed starting points are reduced by 7.93% and 15.54% as compared with the case with fixed starting points.
Multi-Branch Convolutional Auto-Encoder Model For Cross-Domain Person Re-Recognition

Cross-domain person re-recognition is very important for the applications of intelligent video surveillance. To further reduce the cross-domain differences, a new multi-branch convolutional auto-encoder based cross-domain person re-recognition network (MCAENet) is here proposed. Firstly, the reconstructed feature map is utilized as an input of the generative model module, and reconstruction loss and divergence loss are utilized to extract domain specific features and unknown domain features; then, the domain invariant feature guidance branch is introduced to enhance the robustness of domain invariant features; finally, identity loss is introduced and combined with the triple loss to constrain the distance between different pedestrians, so as to extract representative features to distinguish different pedestrians. Extensive experimental results demonstrate that the proposed cross-domain person re-recognition method based on convolutional -encoder achieves the best identification accuracy in various environments.
Few-Shot Segmentation based on Global-cross Attention

As one of the major problems in the field of computer vision, few-shot segmentation has been always in the spotlight. The goal of few-shot segmentation is to employ a few samples to train a model so that it can segment the same kind of query images. However, features of images are invariably the crux to achieve semantic representations. For the sake of extracting more features and further improving the accuracy of semantic segmentation, we propose a network based on global-cross attention. The network obtains global information of the images through the global-cross attention module and correlates local regions to generate more detailed knowledge. The two modules cooperate jointly to improve the performance of the network via feature fusion. We conduct a series of experiments on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> to evaluate the effectiveness of our proposed network and achieve better performance.
Energy optimized D* AUV path planning with underwater digital terrain and ocean current environment

Autonomous Underwater Vehicle (AUV) path planning in the marine environment is different from that in the terrestrial environment. It is important to extend the map to a three-dimensional environment and consider how ocean currents affect the planned path in addition to energy use and safe obstacle avoidance. To solve the above problems, Digital Elevation Model (DEM) is introduced to build a three-dimensional map, and on the basis of the original cost function, obstacle cost items and steering angle cost items are added as constraints to ensure the safety of AUV navigation. Considering the huge impact of ocean currents on AUV energy consumption, this paper establishes a cost model for the impact of ocean currents on AUV energy consumption, and applies it to the D* path planning algorithm. It can be seen from the simulation experiment that AUV can use ocean current to reduce energy consumption. Simulation results show that the improved algorithm can plan the optimal path of energy consumption.
Complex Multistep Manipulator Planning Algorithm Based on Improved UOF

The manipulator is challenging to perform multistep tasks, such as block stacking. Universal Option Framework (UOF) has a high success rate in completing multistep tasks. The Deep Deterministic Policy Gradients (DDPG) algorithm based on Hindsight Experience Replay (HER) in UOF trains low-level policy. However, it has a low success rate, long convergence time, and low training efficiency for complex tasks. To solve the above problems, this article proposes the UOF algorithm for improved DDPG and the UOF algorithm for improved HER. The improved DDPG algorithm suggests the soft double estimation softmax operator based on UOF and introduces it into the DDPG algorithm. By improving the deviation of the DDPG algorithm, the success rate of complex tasks is increased, and the task convergence time is significantly shortened; By introducing curiosity and proximity into the improved HER algorithm, the failure experience of each learning stage is effectively utilized to solve further the sparse reward problem, which significantly enhances the training efficiency and the success rate of complex tasks. Experiments on block stacking tasks show that the improved method can effectively complete complex tasks.
Evolutionary Game of Manufacturing Traceability System Based on Blockchain

The scale of manufacturing industry is huge, and the production of a product requires the cooperation of many enterprises. Based on blockchain technology, for the traceability of manufacturing production data, this paper designs a reasonable incentive method to encourage enterprises to actively upload manufacturing data and build a perfect blockchain traceability platform. Then, the paper establishes a game model for enterprises to upload data, and uses evolutionary game theory to analyze the model to study the strategy selection process of enterprises under the blockchain traceability system. Finally, use simulation to verify the evolution path of the game under different parameters.
TaCoTrack: Tracking Object With Temporal Context

The performance of visual object tracking generally depends on the extracted information of continuous frames. However, existing trackers cannot leverage temporal contexts to extract enough information from frames and does not adapt well to various challenges. In this paper, we present a neat and efficient framework, TaCoTrak, which completely exploits temporal contexts for object tracking. The temporal context are employed in two perspectives, the fusion of features and the refinement of search-response features. Specifically, for feature fusion, a dynamic self-adaptive convolution, which provides the capability of spatial feature representation, is designed to fuse the features extracted from multiple input frames with temporal information. For search-response feature refinement, we construct a temporal convolution with weights and bias change with each input. The extensive experiments fully demonstrate the effective and robust performance of TaCoTrak.
SLAM-based Person Following Robot with Online Trajectory Recovery Strategy

The person following robot needs to detect and track the following target, then move by maintaining a safe distance from the following target, and finally complete the following task. This paper proposes a person following robot framework based on YOLOv7 and DeepSORT methods, which can realize the functions of multi-target detection and re-identification after target loss, and solve the problem of low accuracy of target following. Meanwhile, for the problem of autonomous localization and navigation of following robots in unknown environment, a strategy of real-time map building during the following process is proposed to realize autonomous localization and return after complete loss of the target. For the case that the following target is lost at the corner, this paper introduces a nonlinear regression model to achieve automatic target retrieval. In addition, by using a distributed architecture, the computational consumption on the robot side is reduced. Finally, the effectiveness of the proposed method is verified by conducting experiments in an indoor environment.
Multiple Object Tracking algorithm based on cluster cooperative motion constraint

Multi-target tracking is one of the key research fields of computer vision. Existing methods mainly focus on inferring robust and discriminative features for data association based on targets generated by existing detectors. However, these methods independently process each target when generating the trajectory. In contrast, we propose a new multi-target tracking algorithm, which utilizes spatiotemporal cooperative motion constraints. Our algorithm makes use of the cooperative effect of crowd movement, which means that objects in close range tend to move in a more consistent way. We group several objects with consistent mobility into a cluster. We introduce an affinity matrix to measure the synergy and similarity of the movement between targets in the same cluster. When the target is occluded, the affinity matrix can be used to recover the lost track. We evaluated our approach on the MOT challenge benchmark and proved its effectiveness.
Word Embedding of Dimensionality Reduction for Document Clustering

Word embedding models have been extensively used in document analysis. Even though many models have been created for embedding documents into vector spaces, their document clustering performance is not noticeably better than that of conventional bag-of-words representations. This paper proposes a document clustering called Word Embedding of Dimensionality Reduction (WERD) that can be used in conjunction with any word embedding method and can provide a semantic explanation of the clustering outcomes. Stopwords and a lexical reduction are first used to preprocess the documents. A pre-trained embedding model is used to embed documents. Then a dimension reduction is used to reduce the dimension of the embedded data to remove redundant features and create more compact document vectors used as document features for clustering. After clustering, the Non-Negative Matrix Factorization approach extracts the keywords from each cluster to produce semantic descriptions. Numerous experiments on two datasets show that WERD can produce superior clustering results.
Research on Realtime Decision Making Strategy of Swarm Intelligence based Gaming

Modern war has the features of multi-level, high dynamic, high coupling and high complexity. Intelligent situation cognition and tactical decision making based on hierarchical reinforcement learning are the future development trend. To face the current challenges, this paper studies the lightweight problem of situation cognition and realtime decision making strategy in complex incomplete information dynamic game, focusing on game space reduction, multi-level situation cognition and collaborative strategy optimization. The game decision making model driven by data and knowledge is constructed, and the level of game strategy is improved through self-gaming of reinforcement learning, to gradually reach the level of human commander.
ClassDegSR: A Fast Network for Degraded Image Super Resolution Based on Degrade Degree Classification

Image degradation, which is caused by imaging devices instability, brings great challenges to single image super resolution (SISR). However, the existing degraded image super resolution (DegSR) networks reconstruct all image blocks via one complex network, which ignore degradation degree of image blocks and result in low reconstruction efficiency. Therefore, this paper proposes a fast network for DegSR based on degrade degree classification, dubbed ClassDegSR. Specifically, image blocks are classified based on degradation degree via a Class- Module, which are further processed by different scale branches via a DegSR-Module. Moreover, a three-stage training strategy which utilizes pretrained modules is proposed to enhance the training robustness. According to simulation results, ClassDegSR effectively improves reconstruction quality and efficiency, which has practical value in systems with tight computing resources. And the code is available at https://github.com/vanwd628/ClassDegSR.
3D Contextual Transformer &amp; Double Inception Network for Human Action Recognition

The 3D Contextual Transformer & Double Inception Network called CoTDIL-Net is proposed for human action recognition. The spatio-temporal enrichment module based on a 3D Contextual Transformer (CoT3D) is proposed for enhancing the features of adjacent frames. In addition, 3D Inception and 2D Inception are combined to form the feature extractor called DIFE for capturing short-term contextual features. Moreover, the LSTM is used to obtain long-term action change features, and a multi-stream input framework is introduced to obtain fuller contextual information. It aims to obtain multi-scale spatio-temporal features compared with single convolution methods, where CoT3D combines contextual action information, the DIFE captures short-term features while LSTM fuses long-term features. The experiments are carried out on a laptop with 32G RAM and a GeForce RTX3070 8G GPU by using the KTH dataset, and the results show a recognition accuracy of 97.2%. The obtained results indicate that the proposed CoTDIL-Net promote the convolutional structure understanding of human actions changes.
A Constant Flow Air Sampler Based on Interval Type-2 Fuzzy PID Controller

A constant flow air sampler with closed-loop control is designed to increase the accuracy of pollutant detection. Orifice plate and differential pressure sensor are used as the flow measurement device instead of float flow meter. To improve the adaptability of the air sampler to various flow rates and collection devices, the air sampler is equipped with an interval type-2 fuzzy PID controller. The simulation results show that in comparison to fuzzy PID controller and PID controller, the interval type-2 fuzzy PID controller performs better. The experiments on the real air sampler system show that the interval type-2 fuzzy PID controller has better performance under different flow rates and different loads than traditional PID controller.
Micro Expression Recognition based on Graph Convolutional Networks with LSTM

To improve the recognition rate of micro-expression algorithms, we propose an algorithm based on Graph Convolutional Networks with Long Short-Term Memory(LSTM). The algorithm makes full use of the spatial-temporal features of micro-expressions, first extracting the spatial features of micro-expressions through graph convolutional networks, then using LSTM to extract its temporal parts. Extensive experiments on MMEW and SAMM datasets demonstrate that our proposed method improves the accuracy of micro-expression recognition.
Valuation of Convolutional Neural Network in Intelligent Computer Games

Many problems in incomplete information games also exist in our daily life. It is of great significance to solve the problems in our daily life and improve the quality. Texas Holdem is a typical card game with incomplete information. For Texas Holdem a valuation algorithm model based on convolutional neural network is proposed. To train the model using the game history between the masters, to achieve the purpose of learning master skill. The game procedure of the valuation model plays the game with the game procedure designed by the predecessors, and the experimental results show that the convolutional neural network valuation method of learning human master experience can provide better decisions and enhance the Texas Holdem game procedure.
Learning Prototype from unlabeled regions for Few-shot segmentation

Few-shot segmentation (FSS) uses only a few labeled images to learn the method of segmentation rather than training on huge dataset to find satisfactory parameters. Existing methods pay more attention on labeled data leading to the waste of unlabeled data, i.e., potential novel classes are treated as background on training phase. Our method aims to lighten this problem and improve the feature embedding space of all classes. In our work, we propose a novel framework to learn prototypes of labeled and unlabeled classes. When training on support-query pairs, we introduce an additional prototype generating branch that exploits unlabeled classes using a clustering-based method and a new method for prototype rectification on unlabeled classes to promote stability of prototypes. Above all, our prototype generating technique has the ability to utilize unlabeled classes improving the embedding space. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms the previous state-of-the-art by a margin of 1.13% mIOU on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and 2.15% mIOU on COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> , according to our extensive experiments on two FSS benchmarks.
Offensive and Defensive Attention Reward in Reinforcement Learning for Football Game

In recent years, deep reinforcement learning (DRL) algorithms have made a lot of impressive progress in video games. The launch of Google Research Football (GRF) challenges these well-known DRL approaches in the territory of human-like sports games, which demands both the agent’s individual competence and cooperation strategies with sparse rewards provided. Most of the current solutions have huge requirements in computing resources for self-play or high-quality expert data for imitation learning. In this work, we design an efficient algorithm to avoid these issues. Our method takes full advantage of the built-in AI robots which have a human-level cooperative intention but lack dribble and shoot abilities. The former intention is challenging for DRL to learn, while the latter abilities are feasible for DRL. In this paper to overcome the challenges, we first adopt the attention mechanism to model the offensive and defensive intention of the built-in AI. Secondly, we reshape the sparse reward of GRF by adding an auxiliary reward signal generated from the attention network outputs. Finally, the attention reward instructs the agent to produce more cooperative strategies in the DRL training. Experiments show that our attention reward generator can accelerate the GRF agent training process and our method achieves better performance than the on-policy DRL baseline in the single-agent 11 vs. 11 football scenario.
Design of RBF Neural Network Based on Entropy and Sensitivity Analysis

Aiming at the problem of how to determine the hidden layer structure of radial basis function (RBF) neural network, an RBF neural network algorithm based on entropy and sensitivity analysis is proposed to optimize the network structure. Firstly, an entropy-based initial clustering is proposed to get the number and position of the initial clustering centers which serve as the input to the K-means algorithm. Secondly, a hidden node deletion method based on sensitivity analysis is proposed to analyze the weight and standard deviation between the hidden layer with the output layer, and the hidden nodes which have low contributions to the network will be removed. Simulation experiments on the nonlinear function approximation and the nonlinear dynamic system identification are set to verify the proposed method. Experimental results show that the proposed method achieves stronger approximation ability and better identification effect than the state-of-the-art methods.
Initial steam flow prediction and control optimization for waste incineration plants based on machine learning

As the demand for electricity in modern cities continues to grow, the construction of smart power plants has become a key focus for the sustainable development of our power plants. Daily power plant operations generate a large amount of operational data, mostly monitored for normality without being further explored. With the development of artificial intelligence, machine learning can be used to fit the relationships between the various parameters simply and efficiently. Currently, thermal power generation is China's primary form of power generation. In this paper, we take a waste incineration plant as an example and use XGBoost and the SSA (Squirrel Search Algorithm) to predict and optimize the initial steam flow of the plant.
Research and Simulation of an Improved Path Planning Algorithm based on the Intelligent Watering Robot

Taking the watering problem of multi-potted plants as the research object, the automatic planning path of intelligent watering robot is used to solve the problem of automatic and efficient watering, avoiding the situation of routing and repeated watering of watering pipes. This paper proposes an improved path planning algorithm based on fixed path and automatic path planning to achieve efficient and accurate watering for multiple potted plants, especially for personalized watering of rare potted plants. Through the MATLAB simulation experiment, the effectiveness of the improved path planning algorithm proposed in this paper is verified. Through the analysis in different situations, the algorithm shows better accuracy and rapidity.
Single-Shot Cascade Bounding Box Refinement Neural Network for High Quality Object Detection

This paper proposes a novel one-stage detection method with cascade bounding box refinement, called CasDet. Generally one-stage detection methods generate less accurate bounding boxes than two-stage methods. Motivated by the location refinement of bounding boxes of two-stage methods, we present a cascade bounding box refinement for one-stage detection networks. By refining the detection results for multiple times, CasDet can generate high quality bounding boxes. To further improve the feature representation, we present a multi-scale feature aggregation structure (MBFA). By fusing features with adjacent three scales and adding multiple top-down and bottom-up aggregation paths, MBFA introduces rich contextual information and local details into each detection feature. The experimental results on COCO benchmark confirm that our CasDet outperforms existing state-of-the-art detection methods and achieves a better balance between speed and accuracy.
Texture feature extraction and classification based on improved LBP

The traditional LBP (Local Binary Pattern) algorithm has the problems of poor sensitivity to noise and low robustness to manic point interference when extracting texture features. An Improved Local Noise Robustness Binary Pattern (ILNRBP) is proposed in this paper. Using this algorithm, the gray value of pixels in each field is replaced by the average value of four neighboring pixels, and the difference between the gray value and the mean value of the central pixel is calculated. Then, the binary value of the difference between the difference and the threshold value T is quantized to get the binary string, and the texture characteristic value of the corresponding defect is obtained. The obtained texture feature vectors are sent to the SVM model for classification, and the purpose of classification is finally achieved. The experimental results show that the improved method integrates the texture direction information into the texture feature extraction, and the discrimination effect is more obvious, and the anti-noise robustness of texture classification is improved.