Reviewerly/exHarmony huggingface dataset:
An Improved Method and Application of Recurrent Convolutional Neural Network With Self-attention Mechanism

In recent years, convolutional neural network (CNN), as a representative neural networks in the area of deep learning technology, has been widely used in machine vision such as object recognition and image processing. In this paper, we introduce a more flexible and more accurate recurrent convolutional neural network (RCNN) than traditional CNN for object recognition. But RCNN is restricted to locality and lacks the understanding of the global context. Inspired by the research of cognitive science and neuroscience, we put forward a recurrent convolutional neural network integrated with self-attention mechanism (A-RCNN) to address this issue. Unlike most methods that combine self-attention as an enhancement of convolutional layers with CNN, our method introduces the self-attention mechanism as an independent layer into the RCNN. We evaluate our constructed A-RCNN on the publicly available CIFAR-10, CIFAR-100 and MNIST datasets. The analysis of experimental results shows that our proposed A-RCNN has significantly improved accuracy on object recognition tasks compared with the original RCNN and CNN.
Dynamics parameter identification of industrial robots based on immune algorithm

Dynamics parameter identification is a key factor and a difficult area in the development of robot motion control technology. In order to obtain accurate dynamics parameters, an integral identification method combined with the immune algorithm is studied and experimented with. Firstly, the dynamics model of the industrial robot is derived from the Newton-Euler iteration method. Then, taking the condition number of the observation matrix as the optimization index, an ideal excitation trajectory is optimized by the immune algorithm. Butterworth zero-phase low-pass filter and the weighted least square method are used to solve the minimum parameter set of dynamics. A new validation trajectory is designed using the fmincon toolbox to cross-validate the identified dynamics model. Finally, the above identification algorithm is tested with Inexbot LG-607L robot in China as the experimental object. The experimental results show that the immune algorithm can calculate the excitation trajectory with a lower matrix condition number than the fmincon method and the genetic algorithm. And the parameter identification effect of the integral method is accurate.
AI-based detection system of resident’s behaviors in automatic trash sorting booths: a background computing-based solution

In order to track the serious problem of garbage siege, four-categories garbage sorting systems have been increasingly installed and employed in urban communities. However, manual supervision of trash delivery behaviors is costly, inefficient, and unsustainable from the long term. AI-based automated garbage sorting booths based on computer vision technology become an attractive solution. Under the four categories sorting system, the sorting and treatment of perishable or organic waste belong to the most challenging part. For the delivery of perishable waste in automatic garbage sorting booths, the key of AI-based solution is to accurately identify residents’ abnormal delivery behavior using object detection models. The composition of organic waste is extremely complicated, making it difficult to employ the object detection model in real applications. Therefore, this paper proposes a solution to use the YOLO model to identify non-organic objects such as garbage bags in the perishable barrels. The proposed solution in this study adopts the backend computing architecture and employs the multi-scale YOLOv4 model as the core, which can effectively identify the abnormal delivery of perishable waste. This system has been applied in dozens of intelligent waste delivery booths in Yiwu City, and has achieved satisfactory results.
The edge detection optimization considering existing problems of Canny algorithm

Aiming at the problem of serious manual intervention, poor adaptive ability, and gradient calculation’s low accuracy of Canny algorithm in filtering and threshold detection. An improved edge detection algorithm is put forward based on Canny. Firstly, adaptive smoothing filtering method is used to reduce sensitivity to noise. Secondly, accuracy of edge detection is improved by calculating gradient amplitude in eight directional templates. And non-maximum suppression is used to eliminate invalid pixels. Finally, an improved threshold detection algorithm is adopted to improve the uncertainty of manual threshold selection and improve the accuracy of edge detection. In terms of detection, three parameters including Structural Similarity Index Metric (SSIM), Mean Squared Error (MSE) and Peak Signal to Noise Ration (PSNR) are used to evaluate the similarity of classic images, then the wheelset tread damage problem of high speed trains is analyzed as practical engineering application. The experimental results show that under the condition of 15% salt and pepper noise, after denoising, SSIM of the image is improved by 5.3192%, MSE is improved by 4.6887%, and PSNR is improved by 0.6216%. The following conclusions are drawn: the algorithm proposed this study is optimized based on Canny algorithm, which can effectively suppress salt and pepper noise, reduce the appearance of false edges, and the image edge details are more completed after processing.
Split-and-Merge Based Simultaneous Input and State Filter for Nonlinear Dynamic Systems

This paper investigates the problem of simultaneous input and state estimation (SISE) for nonlinear dynamic systems. By the augmented state approach, we convert this problem into a standard filtering problem. Then, the split-and-merge technique is utilized for the augmented state estimation. Based on this, a novel split-and-merge based simultaneous input and state filter is developed in order to enhance the ability of dealing with highly nonlinear systems. Simulations demonstrate the effectiveness and efficiency of the proposed filter.
Finite Time Sliding Mode Synchronization of Recurrent Neural Networks for Heterogeneous Fractional-Order Hyperchaotic Systems Based on PSO Algorithm Optimization

The controller with finite time sliding mode surface is proposed, which is to make the hetero-structured fractional order hyperchaotic system fully synchronized. At the same time, RNN estimates the uncertain external interference. By designing an adaptive compensator, the influence of uncertainty is eliminated, and the controlled system achieves asymptotic stability. According to Lyapunov stability analysis, the adaptive law is obtained, and the subsequent parameters of RNN are optimized. PSO algorithm optimizes the control parameters.
Research on Adaptive Control Approach of Firefighting UAVs with Fire-Extinguishing Bombs

Due to the strong mobility and small size, fire-fighting UAVs with fire-extinguishing bombs are ideal for handling high-rise building fires. However, the launch of fire-extinguishing bombs causes not only undesirable recoil but also the significant mass variation of the vehicle. These adverse factors, together with the complicated air turbulence and extreme heat in the high-rise fire spot, make the stable flight control of fire-fighting UAVs a challenging task. In view of the above facts, a model reference adaptive control approach is presented to deal with the flight control problem of fire-fighting UAVs during fire-fighting missions. Simulation results show the effectiveness of the proposed approach.
Research on target detection and tracking algorithm for campus driverless vehicle based on improved DeepSORT

Compared with other structured road scenes such as highways, the campus scene has the characteristics of multi-target, multi-cluster and multi-occlusion, the detection accuracy of traditional detection algorithms is low in this scene. In order to solve this problem, this paper proposes an improved DeepSORT target detection and tracking algorithm. The improved algorithm uses the YOLOv4-tiny algorithm for target detection and obtains the detection frame, then take the detection result as input, using the Kalman filter to predict the target trajectory, the detection and the prediction frames of the target are matched by the Hungarian algorithm. It uses DIOU (Distance Intersection Over Union) instead of IOU (Intersection Over Union) to perform correlation matching between unsuccessfully matched trajectories and detection results, followed by the Kalman filter update. Finally, the experimental of the campus driverless vehicle shows that the improved algorithm MOTA (Multiple Object Tracking Accuracy) increased by 8.6% and MOTP (Multiple Object Tracking Precision) increased by 1.5%.
Interpolation and simulation of autonomous driving camera data for vehicle position synchronization

In order to meet the data requirements of the virtual simulation test of autonomous driving, we use the camera’s single-sample video data, interpolate frames to generate multiple camera simulation data. And then realize the simulation of extended front-end perception function at the data level. This paper proposes a video sampling frame simulation reconstruction mechanism based on sampling vehicle pose information in real scenes. Calculate the target position of the simulated vehicle according to the simulation requirements, and establish a simulation node on the sampling path. Taking the position difference between the simulated node and the real node as the offset, the DAIN algorithm is used to insert the image data into the target node. It is possible to realize simulation data generation of autonomous driving camera with variable vehicle speed/sampling frequency. Combined with the camera’s internal and external parameters, the coordinate system transformation of the annotation results is carried out to realize the inheritance of the annotation results of the simulation data. This paper combines the nuScenes open source database to test the authenticity of the synchronous simulation data results. The results show that the mean SSIM of the synchronous simulation image data and the real data is 0.71684, indicating that the simulation data has high authenticity. Based on yolov4, the perceptual function of the simulated data is verified. The average value of the SSIM of the simulated image data and the real data recognition frame is 0.984504795, and the perceptual recognition results are similar. The synchronous mapping result of the marked 3D-BOX box to the simulation data space is correct. Camera simulation data can well meet the needs of autonomous driving development and testing in perception and recognition. It can provide data support for autonomous driving simulation test.
SiamITO: A Lightweight Siamese Network for Infrared Tiny Object Tracking

Siamese networks have developed rapidly and achieved great results in the field of object tracking. However, most Siamese-based trackers focus on tracking generic object and little research has been done on tracking infrared tiny object (ITO). ITO has very few pixels in the infrared image and lacks appearance information, which makes ITO tracking a challenging task. To track single ITO, we propose an efficient Siamese-based network, dubbed as SiamITO. SiamITO contains three mainly modules: feature extraction backbone, feature fusion module and multi-attention module. Feature extraction backbone contains several convolution layers and residual connections to ensure the effective extraction of features and retention appearance information. Feature fusion module uses multiple layers of output to integrate multi-level information. The multi-attention module computes self-attention of ego-branch and cross-attention of each-branch to learn contextual information for better distinguishing the object from background. To demonstrate the effectiveness of SiamITO, some experimental analyses have been carried out in a series of real ITO video sequences. Compared with many SOTA methods of single generic object tracking, the proposed method significantly improves tracking accuracy of ITO while maintaining high inference speed.
Environment-adaptive Bat Algorithm For UAV Path Planning

The main purpose of this paper is to solve the path planning problem of UAV in complex environment. In order to get rid of the local extremum and enhance its convergence ability, this paper proposes an environment-adaptive bat algorithm (EABA) with proper convergence ability in different environments. The convergence ability of the EABA can be adjusted by the particle swarm optimization (PSO) algorithm. At the same time, the obstacle avoidance is achieved in the path planning. In the simulation experiment, the proposed EABA was compared with the traditional BA and PSO. By evaluating the generated paths, the simulation results show the effectiveness and good performance of the proposed EABA.
Pedestrian Tracking Algorithm Based on IRB-JDE

There are several problems in the existed pedestrian tracking algorithms, such as missing detection and false detection of small-size pedestrians and occluded pedestrians, frequent pedestrian identity jumps, and track loss. In this paper, an improved pedestrian tracking algorithm called IRB-JDE (Joint Detection and Embedding Improved by Res2NeXt50 and BYTE) is proposed. In specific, an improved backbone network, namely Res2NeXt50 (I_Res2NeXt50), is proposed by improving the residual network structure of ResNet50. The improved backbone network has developed the tracking accuracy of occluded pedestrians and reduced the number of pedestrian identity jumps. Secondly, a multi-scale fusion network is presented to get the location ability of small-size pedestrians better by integrating the PAN structure on the FPN structure. Finally, a data association algorithm named BYTE is introduced to improve the reliability of data association and decrease the quantity of track loss. Ablation experiments demonstrate the efficiency of the abovementioned improvements in increasing the accuracy of pedestrian tracking. In comparison with the existed algorithms, the IRB-JDE performs better on the MOT16 dataset according to the commonly-used evaluation indicators.
Combined calibration method for large field of view multi-camera system

In the calibration process of multi-camera system with large field of view, aiming at the problem of self occlusion in the plane calibration method and the problem of low calibration accuracy of one-dimensional calibration methods, a calibration method of multi-camera system based on the combination of plane target and one-dimensional target is proposed. Firstly, a mathematical model of multi-camera calibration is established with pin-hole model and rigid body transformation theory; the internal parameters and distortion coefficients of each camera are calibrated by two-dimensional plane target; on this basis, analyze the uncertainty of imaging error of circular feature points, cost function of random sample consensus algorithm(RANSAC) is improved. Using the camera imaging model, the essential matrix between cameras is estimated, and the initial value of camera external parameters is decomposed and determined; lastly, multiple nonlinear optimization algorithms are used to refine and obtain the camera matrix, The simulation and actual experimental results show that the multi camera system combination calibration method proposed in this paper can realize the parameter calibration of four camera measurement system, and the measurement space is 2000mm×2000mm×2000mm, the 3D reconstruction error is less than 0.15mm. Compared with the traditional calibration method, it has more accurate calibration accuracy.
A new method based on YOLOv5 for remote sensing object detection

Objects on remote sensing images usually have the characteristics of complex background and extremely large scale changes. This makes most general object detection algorithms designed to cross-scale datasets unable to achieve ideal results in remote sensing images. In this article, we propose a new method based on improved YOLO network to solve the difficulties mentioned above. We introduce a new module to extract multi-scale information to enhance the ability of the network to detect objects with different scales in complex background and a new FPN named AG-PAN which use two top-down pathways to compensate for semantic information loss and attention mechanism to focus on the distinctive features of the objects. Soft-CIoU-NMS be used to replace the commonly used NMS to generate better detection result. Extensive experiments are conducted to demonstrate the effectiveness of our approach on the COCO, RSOD (Remote Sensing Object Detection) dataset. The results show that our method outperforms original YOLOv5 by 3.1% AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> and other state-of-the-art methods in RSOD. In COCO dataset, our method has 2.6% improvement in AP and 2.9% improvement in AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> . The experimental results show that our method significantly improves the performance of the object detection algorithm in remote sensing images.
Research Demonstration of Positive and Negative Sample Classification Algorithm for Insulator Multi-Angle Target Detection Based on Faster-RCNN

Aiming at the problem that the traditional Faster RCNN target detection algorithm fails to reflect the real shape of the insulator from multiple angles and the horizontal IOU algorithm cannot classify positive and negative samples from multiple angles, an improved insulator target detection algorithm based on the traditional Faster-RCNN target detection algorithm is proposed. In this algorithm, the angle parameter is added to the horizontal and vertical rectangular detection box, and the parameter is added as a training parameter to the loss function for iterative regression, which can effectively solve the problem that the conventional Faster-RCNN target detection algorithm cannot correctly reflect the insulator shape. After adding the angle parameters, the horizontal IOU algorithm is no longer suitable for the classification of positive and negative samples. Therefore, the Euclidean norm positive and negative sample classification algorithm is designed to solve this problem. The experimental analysis shows that the multi-angle detection frame with the addition of Angle parameter and the replacement of the IOU algorithm can accurately reflect the shape of insulator targets in any angle direction. Compared with the AP (46.87%) of the horizontal IOU algorithm and the AP(65.00%) of the inclined IOU algorithm, the AP(75.06%) of the proposed algorithm has better comprehensive performance.
Research and Application of GAN-GRU in Bridge Monitoring Data Cleaning

With the increase of sensors in the bridge health monitoring system, huge amount of time series data are continuously generated and recorded to provid strong support for bridge health monitoring. During the acquisition of time series data, abnormal data can easily be generated due to sensor failure or improper transmission and storage, which affects the performance of the monitoring system. There is a strong dependence between the before and after values of time series data. Therefore, this paper will propose a fusion model based on GAN and GRU by combining the generative capability of generative adversarial network (GAN) with the predictive capability of gated recurrent unit (GRU). The model is very suitable for gated recurrent neural network for sequence data modeling to build the internal network structure, while the discriminator can further improve the prediction accuracy of the network at the sequence level and reduce the impact of error accumulation on the prediction performance of the network. After the network training is completed, the generator can generate forecast values that match the original time series data, thus completing the replacement of abnormal data series. Through the analysis of the experimental results, it is found that the GAN-GRU model can effectively predict the deflection data of the bridge. In the abnormal ten-minute deflection data, the RMSE was 0.0778. Practice shows that the fusion of GAN-GRU model enhances the ability of bridge health monitoring system to deal with abnormal data, and effectively improves the reliability of bridge health monitoring system.
Temperature prediction of roller kiln based on mechanism and SSA-ELM data-driven integrated model

Temperature prediction in roller kiln is an important problem for lithium-ion battery cathode materials. However, since the complexity of process and industrial noise, it is difficult to obtain accurate prediction models. This paper presents an integrated modeling method that combines mechanism with data-driven model. Firstly, on the basis of heat transfer mechanism and heat balance theory, the mechanism model of the sagger temperature in the roller kiln is established. Then, aiming at the estimation error caused by industrial process complexity and industrial noise, a data-driven error compensation model based on real-time process operation data is constructed. In view of the large lag of temperature change in roller kiln and the high correlation and strong nonlinearity between process variables, the error compensation model of sparrow search algorithm optimization extreme learning machine SSA-ELM is established. Then the mechanism model and error compensation model are combined to obtain a comprehensive temperature prediction model. Finally, the numerical experiments using real data show that the comprehensive prediction model has high prediction accuracy.
An Uncertainty Network Working Mechanism Analysis Method based on Local Interpretable Model-agnostic Explanations

In recent years, Deep learning has been widely used in different fields which learns the skills from dataset, however the parameter and decision logic is opaque for human. The trustworthy of deep learning model has attracted much attention of researchers, in that uncertainty is an essential part of trusted artificial intelligence. Therefore uncertainty analysis is an important part of the basic theory of deep learning that needs to be researched. The stochastic differential equation network is able to predict the uncertainty by the design of network and training, but it’s doubtful for the uncertainty quantification. To analyze the deeply work mechanism in uncertainty prediction, we propose the an uncertainty network working mechanism analysis method based on local interpretable model-agnostic explanations. The explainable artificial intelligence method is applied to expose the decision logic of uncertainty prediction. By the experiments we have proved that the uncertainty generated by the stochastic differential equation network is related to the performance of prediction, and they have a positive correlation.