Reviewerly/exHarmony huggingface dataset:
Multi-scale feature feedback network for single image super-resolution

Aiming at the problems of the existing single image super-resolution (SISR) reconstruction methods, such as insufficient feature utilization and weak ability to recover image high-frequency details, we propose a multi-scale feature feedback network (MFN) for SISR. First, multi-scale convolutional kernels are used to extract features of different dimensions in the image. Thus, the ability of the network to extract features of different scales early is improved. Then, we combine the dense network to design a group of multi-scale projection units to explore the correlation between high and low-resolution image features from multiple scales. Each group of projection units consists of a multi-scale up-sampling block and a multi-scale attention block (MAB). The MAB focuses on high-frequency features related to reconstructed image edges and textures. Finally, we used a constrained recurrent neural network to implement the feedback mechanism to improve the feature extraction capability of the network. Extensive experimental results demonstrate the effectiveness of our approach, which has advantages over other state-of-the-art SISR methods in terms of restoration quality and network complexity.
Finite-time Target Function Tracking of Quantum Systems with Impulsive Control Fields

In this paper, the finite time tracking issue for a class of closed quantum systems under the action of impulsive control fields is addressed. The aim is to steer the state of the quantum system to the trajectory of a periodic time-varying target. By using the Lyapunov direct method and considering a finite-time control strategy, the modified impulsive tracking controllers are designed such that the error dynamical system converges to zero in finite time. While finite-time tracking implies optimality in convergence time and has better disturbance rejection. Numerical simulations are given to illustrate the effects of system’s trajectory tracking, and also given to demonstrate the superiority of the control laws which are proposed.
Prediction of Boiler Heat-Conducting Oil Temperature Based on Multi-Modality Fuzzy Cognitive Maps

Accurate and interpretable time series prediction is of great significance in production, it can help people deal with boiler abnormalities timely and accurately, and provide guarantee for safe production. This paper proposes a time series prediction method based on multi-modality fuzzy cognitive maps with data preprocessing, which can soundly achieve the purpose. As the model is for solving practical problems, the experiment in paper uses real boiler data to improve objectivity. Firstly, the outliers in the dataset are replaced with reasonable data, then fuzzify the processed time series. Subsequently, the subsequences are chosen in the time series, and the fuzzy cognitive maps model is established on each subsequence. Finally, the model outputs of each subsequence are mixed as the prediction results and obtaining the forecast results. The experimental results contracted with Long Short-Term Memory (LSTM) show that the proposed model can well predict the changing trend of the actual industrial heat-conducting oil temperature of boiler.
Image Based Visual Servoing Model Predictive Control of Manipulator

Visual servoing as an important implementation of machine intelligence has been widely studied. While in the visual servoing control task of multi-joint manipulator, the task often fails due to the occurrence of camera retreat phenomenon. The present paper considers the combinations of the image based visual servoing and model predictive control (MPC) scheme to control manipulator. The visual servoing control of the manipulator system includes the object detection system, visual servoing controller and manipulator. By considering the MPC scheme with dynamic weight matrices, the phenomenon of camera retreat in the process of manipulator visual servoing is properly dealt with. The designed methods are verified in the simulation experiments.
Self-supervised Multi-view Stereo via View Synthesis Representation Consistency

Self-supervised Multi-view Stereo technique aiming at the reconstruction of 3D models from 2D images has made great progress. However, existing methods are mainly based on the premise that the corresponding pixels between different views have similar characteristics. However, in the actual scene, we often suffer from the interference of the occlusion area and non-Lambert surface, which reduces the quality of the depth map obtained by depth estimation, and also affects the accuracy and completeness of the final generated point cloud model. In this paper, we propose a new self-supervised framework that uses joint feature consistency and view synthesis representation consistency to construct the self-supervised signal. Additionally, we add data joint representation augmentation mechanism branch to capture the similarity degree of corresponding pixel points between images, so as to improve the unsatisfactory situation faced in the process of large-scale real depth acquisition. The results of experiments on DTU dataset show that our proposed method has good performance, even better than some supervised methods. Furthermore, the results of experiments on Tanks&Temples dataset prove that it also has good generalization ability.
Pedestrian tracking and counting based on YOLOv4 and DeepSORT in Subway Stations

Real walking parameters of pedestrians are the basis of research on pedestrian dynamics. The detection and tracking technology provides the possibility of accurate walking parameters. To tackle the problem of lower detection accuracy of small-scale pedestrians, the YOLOv4 algorithm has been improved in two aspects. Then, the improved YOLOv4_I model acts as the detector of DeepSORT algorithm for pedestrian tracking and counting. After the improvement, the YOLOv4_I algorithm performs well in both the public and private datasets. The mAP is 90.61% in the private dataset, raising about 3.42%. Detection speeds reach 28.2 and 18.2 fps for processing images and video, respectively. Based on the YOLOv4_I-DeepSORT algorithm, trajectories and walking parameters of pedestrians can be obtained. Results show the average speed of pedestrians on stairs in subway stations follows the normal distribution, the mean value is 1.27 m/s, and the standard deviation is 0.33 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
An Advanced Control Method of Manipulator Based on Visual Servo Control

At present, because of the shortcomings of traditional visual servo control methods, such as slow convergence speed and camera calibration, an advanced visual servo control method is based on the combination of fuzzy logic and Kalman filter, which is proposed in the paper. In the control method, Kalman filter algorithm is used to estimate image Jacobian matrix online, and fuzzy logic control algorithm is used to design variable control coefficient instead of traditional fixed control coefficient, which not only reduces convergence time of visual servo control system, but also avoids complex phase machine calibration, saves a lot of time, and improves work efficiency. Finally, the effectiveness and superiority of the proposed control method are verified by experiment simulation.
Research on USV Route Planning Based on Simulated Annealing-Chaos Adaptive Particle Swarm Optimization Algorithm

In this paper, a simulated annealing adaptive particle swarm optimization algorithm based on the Logistic chaotic mapping model (SA-CAPSO) is proposed to solve the problems of premature, long initialization time, insufficient anti-jamming adaptability, and low convergence speed and accuracy of traditional particle swarm optimization (PSO) for unmanned surface vehicle (USV) route planning in a relatively complex environment. First, in this method, the adaptive coefficient is used to replace the original intrinsic coefficient to improve the velocity distribution of particles. Secondly, the chaos map strategy is introduced to improve the initial division of particles by using its sensitivity to the initial value. Finally, the simulated annealing algorithm (SA) is integrated to improve the optimality of the solution by taking advantage of its probabilistic global optimization characteristics. To evaluate the SA-CAPSO algorithm, we compared the route planning results with the standard Particle Swarm Optimization (PSO), Adaptive Particle Swarm Optimization (APSO), and Chaos Adaptive Particle Swarm Optimization (CAPSO). The experimental results show that the SA-CAPSO algorithm can obtain the planned track curve with good convergence accuracy and speed.
An Improved Siamese Tracking Network Based On Self-Attention And Cross-Attention

Deep Siamese visual tracking network SiamRPN++ is found that its success rate and robustness is unsatisfactory when meeting complex scenes such as occlusion, large deformation, interference of similar objects and long-time tracking. Refer to these, we propose an improvement strategy based on self-attention and cross-attention mechanism. For backbone, we use Channel and Space self-attention modules, and we using different cross channel attention modules between template features and search features in every three RPN modules, finally using special self-attention on similarity feature maps. These tricks effectively suppress interference, improve the features’ quality and make progress in robustness. Comparing with original SiamRPN++ with parameters from official open-source frame, PySOT, our network improves robustness of 3% on VOT2018, accuracy of 2% and success rate of 3% on OTB100.
A multi-strategy valuation model for popular mahjong actions

One of the most important and challenging research directions in artificial intelligence is incomplete information games. Mahjong is a typical representative of incomplete information games. Compared with other incomplete information games such as two-player Texas Hold'em and bridge, mahjong is characterized by many players and high uncertainty in the order and strategy of each player, which makes it difficult for the opponent to make reasonable behavioral decisions by accurately assessing the game situation. In this paper, we propose a multi-strategy valuation model based on popular mahjong: by evaluating the winning rate of the situation and the prediction of the opponent's listening tile, we divide the playing strategies into offensive, defensive and general strategies, and adopt different joint playing models for different strategies. The experimental results show that the proposed strategy has a higher level of decision making under a limited number of hands compared with the previous method of single-play model.
A Single Image Defogging Algorithm Based on Bright and Dark Region Segmentation

The defogging algorithms based on the dark channel prior have a poor effect in the scene with dim brightness or multiple bright and dark regions, and the defogged image is generally dark. In this paper, a single image defogging algorithm based on bright and dark region segmentation is proposed. Firstly, the foggy image is sharpened, and segmented into several super pixels by using the improved SLIC super pixel segmentation method. According to the brightness feature of the super pixels, they are classified into multiple bright and dark regions. Then, the transmittance and atmospheric light are calculated by using the bright and dark segmentation image and the physical model of atmospheric scattering, and the preliminary defogged image is obtained. Finally, HSV spatial brightness adjustment algorithm is used to correct the brightness of bright regions, and multi-scale Retinex algorithm with adaptive weights is used to enhance the brightness of dark regions to obtain the final defogged image. The experiment shows that the method in this paper can effectively avoid the influence of multiple bright and dark regions on estimation of transmittance and atmospheric light, and it has a good effect in ordinary scenes and scenes with dim brightness or multiple bright and dark regions, so it is more applicable than other defogging methods based on the dark channel prior. In addition, the defogging ability of the proposed method is comparable to that of defogging methods based on deep learning, and the real-time performance of the proposed method is higher than that of defogging methods based on deep learning.
Path Planning for Soccer Robots Based on Improved Informed-RRT<sup>*</sup> Algorithm

In the actual soccer robot game, a good path planning result is the premise for the robot to carry out strategic movement. To solve the problems of the traditional Informed-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm, such as long initial path generation time and poor path smoothness, a path planning method based on the improved Informed-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm is proposed to make it suitable for soccer robot walking. First, we propose an initial path sampling based on rectangular sampling range, and then we use multi-threaded double tree growth instead of traditional alternate double tree growth. Secondly, a node expansion method based on simulating multiple resultant forces is proposed, and an adaptive step strategy is introduced. Finally, a method based on path point stretching and sharp angle bluntness is proposed to process path points, and a path with continuous curvature, good smoothness and short path length is obtained. And a series of experiments show that the algorithm in this paper has good efficiency and adaptability for soccer robot.
Optimizing the evaluation parameters of Amazon chess with parallel genetic algorithm

In the process of computer game, in order to improve the accuracy of the situation evaluation of Amazon AI program, an optimization scheme based on parallel genetic algorithm is proposed. This scheme uses a multi-node distributed parallel algorithm to optimize the weight of the parameters of Amazon's chess game evaluation function. The whole optimization system is mainly composed of server and client. Each client uses multithread parallel computing and transmits the result data to the server. Through experiments, the optimization results of the parameters of the situation evaluation function can be obtained in 5 hours. It is proved that the situation assessment is more accurate and the capability of the AI program is significantly improved after the optimization of this scheme.
Text Classification for Fault Knowledge Graph Construction Based on CNN-BiLSTM

In order to make full use of fault historical text data, knowledge graph is used to assist fault diagnosis. Text classification, a fundamental method for constructing knowledge graph, is prone to the overfitting issue when dealing with sparse and small sample data such as fault domain data. Therefore, an integrated neural network model based on CNN-BiLSTM (convolutional neural networks and bidirectional long and short-term memory networks) is proposed in this paper. In this model, CNN layers extract local semantic features while BiLSTM layers integrate contextual semantic information. Then, adversarial training and attention mechanism are introduced to enhance model performance and prevent overfitting. The proposed method is verified on two datasets, general text data set THUCNews and bearing fault dataset. Comparative experiments reveal that the model can push text classification accuracy in the bearing fault domain to 90%, thereby contributing to greater efficiency and lower labor costs for fault knowledge graph construction.
Road Map Assisted Multi-Target Tracking Method for Intelligent Vehicle

Accurate and real-time target tracking is essential to ensure the safety of intelligent vehicles. However, tracking in complex traffic environment is still a challenging problem. Considering that the road map is usually known in the traffic scene, this paper proposes a road map assisted Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter to improve the target tracking performance. By the method, the road area and non-road area are extracted and distinguished from the map firstly, and the targets in the non-road area are removed; Then the number of birth target appearing in different areas is calculated at current time, to update the intensity information of birth target in the road area and non-road area iteratively. Finally, the updated birth target number is used as the state estimation of sensor measurement information at next time. The simulation results show that, compared with the Gaussian mixture probability hypothesis density filter without road map assistance, the proposed method improves the target tracking accuracy and reduce the calculation costs.
An Improved Rapidly-Exploring Random Trees Method for Manipulator Trajectory Planning

To realize the target posture requirement of manipulators and obtain low-cost high-quality trajectories, the trajectory planning problem in joint space is formalized, and an improved Rapidly-Exploring Random Trees algorithm is proposed. The proposed algorithm optimizes the selecting parent procedure and rewiring procedure through the direct connection operation, which is beneficial to reduce the cost and improve the smoothness of the trajectory. Comparison experiments are carried out in simple and complex simulation environment. The experimental results demonstrate the efficiency of the proposed method and its considerable advantages in enhancing the performance of the algorithm.
Super-resolution Reconstruction Algorithm for Depth Image Based on Fractional Calculus

This study presents a new method for super-resolution of depth images by combining fractional calculus and inverse distance interpolation. The method improves edge recognition through fractional differential and solves the problem of missing data in traditional super-resolution algorithms for depth images. Results show that the proposed method outperforms classical interpolation algorithms, with improved PSNR by 3-10dB. The method effectively solves the impact of depth image edge distance on interpolation results, and provides more texture information.
Research on Multimodal Aspect-Based Sentiment Analysis Based on Image Caption and Multimodal Aspect Extraction

Sentiment Analysis is an important task in NLP. In recent years, more and more attention has been paid to Multimodal Sentiment Analysis. For Multimodal Aspect-Based Sentiment Analysis (MABSA), most of the existing methods take the original information of each modality as input and extract each modality’s features and the alignment between them. Based on the existing methods, additional information, including aspect terms and image caption, is extracted. The original and additional information is used as a compound feature so that the model can have a better understanding of each modality and the relationship between them. Experiments show that the method achieves better results on MABSA and its two sub-tasks: Multimodal Aspect-Sentiment Classification (MASC) and Multimodal Aspect-Terms Extraction (MATE).
HGLP: Hierarchical Solver for Combined Task Assignment and Path Finding

Intelligent warehouse based on the multi-robot system significantly improves logistics efficiency. Completing orders usually includes order task assignment and multi-robot path planning. These two processes influence each other. This paper presents a hierarchical task assignment and pathfinding (TAPF) solver, HGLP, for an intelligent warehouse. The high level is genetic algorithm based task assignment, and the lower level is the learning-based multi-agent pathfinding (MAPF) using reinforce to learn the policy. We tested our solver on warehouse-like structured maps of different sizes. The results show that: 1) Our MAPF solver significantly surpasses PRIMAL. 2) HGLP is much faster than the optimal TAPF solver CBS-TA. The difference between the minimum sum-of-cost (SOC) of HGLP and the value of CBS-TA is less than 2%.
Kernel locality preserving projections algorithm based on T-class weighted kernel functions with center kernel alignment

The current data generally has a high dimension and contains many features unrelated to category labels.On the basis of local preserving projection,we proposed a kernel local preserving projection algorithm based on T-class weighted kernel function of kernel alignment.Firstly,CKA algorithm was used to calculate the weight of each kernel matrix,which was used as linear combination coefficient.Combined kernel function was constructed by combining different types of kernel functions to improve the recognition rate and anti-noise ability.Secondly,we use the t distribution in probability statistics to construct the T-type kernel function,which further improves the combined kernel function.Finally,we use the kernel local preservation projectin for identification,and verify it on several data sets.The experimental results prove the effectiveness of the proposed method.
Robust time-varying Kalman predictor for descriptor system with random one-step measurement delay and multiplicative noise

For the linear stochastic descriptor system with random one-step measurement delay and multiplicative noise, the robust prediction problem is addressed. Applying the singular value decomposition (SVD) method, augmented state approach and the fictitious noise approach, the original descriptor system is transformed to new standard system only with uncertain-variance fictitious noises. Based on Kalman filtering and minimax robust estimation principle, the robust time-varying Kalman predictor is presented by replacing the unrealization conservative measurement by the actual measurement in the conservative Kalman predictor. Further, the robustness is proved by the Lyapunov equation approach, i.e., the actual estimation error variance are guaranteed to have minimal upper bounds for all admissible uncertainties. A simulation example about circuits system verifies the correctness and effectiveness of the proposed results.
Integrating Eye-movement Interaction into Spatial Super-resolution Reconstruction

In this paper, an algorithm based on interpolation for eye movement information control image super resolution is proposed. The key of the eye movement system is to obtain the area the user is looking at, and calculate the staring area by constantly checking the kappa Angle information. Firstly, the near-infrared light can be used to calculate the 3D gaze estimation vector through the corneal reflection for optical tracking. Secondly, based on resampling and interpolation methods, this paper implements an adaptive spatial resolution improvement algorithm with arbitrary adjustable multiples, and carries out an adaptive contrast sharpening adjustment. Then, based on the eye-movement interaction information, the two-dimensional plane observation coordinates were calculated through the gaze vector, and the key region being gazed at was calculated according to the most sensitive field of view Angle of the human body, so as to improve the resolution of this region. Finally, the feasibility of the proposed method is tested from qualitative and quantitative dimensions. Experimental results show that the method proposed in this paper can significantly improve the user's interactive visual effect.