Reviewerly/exHarmony huggingface dataset:
Research on path planning strategies for intelligent vehicles

For the planning rationality of intelligent vehicle path planning and trajectory tracking, driving stability, In this paper, the traditional TEB (Timed-Elastic-Band) algorithm is used to plan the initial trajectory based on the desired trajectory, which satisfies the kinetic constraints of intelligent vehicles, has a better time and avoids dynamic obstacles, and the speed jitter caused by excessive acceleration and the front wheel angle jump caused by insufficient lateral sway angle constraints. By improving the traditional TEB algorithm, the secondary planning is carried out on the planned trajectory in the lateral and longitudinal directions to make the obtained trajectory smoother and more reasonable, and the local path planner design is completed. The effectiveness of the method is further verified by the simulation platform of Carsim/Simulink. The results show that the proposed method can ensure the rationality of trajectory planning and driving stability.
Intelligent vehicle detection and tracking system based on improved artificial intelligence algorithm

Aiming at the problems of low detection and tracking accuracy, poor robustness and inaccurate vehicle flow statistics of multi-vehicle types in surveillance video, an artificial intelligence-based vehicle detection and tracking method was proposed. This method reconstructed the feature extraction network of the target detection model YOLOv5s to enhance the extraction of important features of the target and improve the detection accuracy of the detector. Firstly, Swin Transformer module is introduced into the Backbone network to replace part of C3 modules in the original algorithm, so as to enhance the global modeling ability of the model, better capture the context feature information, and expand the model's receptive field. Then, by comparing different attention mechanisms, NAM attention is selected to access the Neck network to enhance the cross-dimensional interaction between the channel and spatial dimensions of information, reduce information loss and enhance the performance of the network. Finally, the feature extraction network part of the DeepSort algorithm was optimized and retrained on the vehicle rerecognition data set to make it more suitable for vehicle tracking. The experimental results show that the accuracy of vehicle detection by the improved YOLOv5s is increased by 1.93 percentage points compared with the original algorithm. Combined with the DeepSort algorithm, the accuracy of vehicle flow statistics under different lighting conditions such as daytime, evening and night can reach 97.2%, 97.5% and 80.4% respectively.
Underwater Image Enhancement Based on Adaptive Correction and Fusion

Underwater applications require clear underwater images, however, blurring and contrast degradation in underwater images are caused by absorption, dispersion, and water mass. To address the limitations of existing color correction and deblurring algorithms, this study proposes a fusion-based image enhancement algorithm. Two images obtained by adaptive channel defogging technique and restricted histogram mapping interval correction technique are used as two inputs to the fusion framework to obtain a new underwater image. We tested our technique with a public dataset. The experimental evidence shows that the technique proposed in this paper can improve the quality and detail of blurred underwater images. This paper also demonstrates that the method has superior deblurring and color correction capabilities than other existing algorithms.
AC-YOLO: A Safety Helmet Detection based on YOLOX

Aiming at the problems of occlusion, low resolution, many small targets and complex background in the existing scene images, which lead to the low accuracy of helmet wearing detection, a new helmet wearing detection algorithm AC-YOLO is proposed. Firstly, in order to solve the problems of limited size, appearance and geometric features of small targets in the existing datasets, a new helmet data set is constructed in this paper. Based on the original YOLOX model, the AC-YOLO model adds the small object detection head, and the original three-scale feature layer is increased to four-scale feature layers. In addition, the AC-YOLO model adds the AC-SR module and the CBAM attention module on the basis of the original YOLOX algorithm, which can extract as many foreground features as possible while fusing multi-scale features. The experimental results show that, compared with the original YOLOX model, the mAP of this algorithm is improved by 1.75%, the precision of wearing helmet is 95.83%, and that of not wearing helmet is 93.45%, which is improved by 2.22% and 1.24% respectively.
A Chinese named entity approach for character semantic enhancement

Named entity recognition is a task in information extraction. In Chinese NER, it is more common to use characters as input. However, the character vector in this model carries less information and cannot represent the word information and polysemy problem. To solve these problems, BERT model, dictionary embedding and graph convolution neural network model are introduced, and a semantic enhancement method for Chinese NER is proposed. In the new method, the character vectors with prior knowledge obtained by BERT are inserted into the dictionary and fused with word-level and word-level features. Contextual semantic information is then obtained by encoding in LSTM, and input into the graph convolutional neural network to obtain the global semantic features of characters. Next, the fused semantic features are input to conditional random field for sequential label decoding. In this paper, model comparison experiments are carried out in four classical Chinese data sets. In the Resume dataset experiment, compared with the comparison model, the three indexes of the new method are optimal. In the experiment of ne and nm data sets in WeiBo data set, the index F1 of the new method is optimal. In the OntoNotes dataset experiment, P and F1 of the new method are optimal.
Retinex-LTNet: Low-Light Historical Tibetan Document Image Enhancement Based on Improved Retinex-Net

Smoothing the background and highlighting the foreground text of document images not only improve the reading experience but also benefits the subsequent document analysis and recognition algorithms. However, most of the past low-light image enhancement methods show less effectiveness while dealing with low-light Tibetan document images. In this paper, we propose an improved Retinex-Net image enhancement network framework for low-light historical Tibetan document images. First of all, to effectively obtain the texture features of the fonts in the document image, we improved the Enhance-Net in Retinex-Net, and introduced the Residual Layer and Dense Connection Block combination block based on the encoder - decoder, so-called Residual Dense Connection Block (RDC Block); Secondly, we constructed a data set containing low/normal-light historical Tibetan document image pairs for network training. The experimental results show that the network not only enhances the image light, but also makes the background and text contrast more vivid. In addition, compared with the original method, the proposed method improves the PSNR and SSIM by 8.1% and 5.8% respectively, reduces the MSE by 21.2%, and achieves better results compared with similar algorithms.
Graph Neural Network in Knowledge Graph aided Recommender Systems

Recent studies have shown that the problems of cold processing and data sparsity in recommender systems can be well alleviated with the help of knowledge graph information. Meanwhile, since the information in recommender systems can be represented by graph data structure, graph neural network has great advantages in recommendatory system. Therefore, graph neural network can better mine the deep relationship between objects in the recommender systems which combines with information from knowledge graph. This paper reviews the application of graph neural network in knowledge graph aided recommendation systems. Specifically, this paper firstly sorts out the related knowledge of knowledge graph, recommendation system and graph neural network, including the cold processing and data sparsity, the structure of knowledge graph, and the basic modules of graph neural network. This paper also summarizes the methods such as KGAT and CKAN used in knowledge graph aided recommendation systems in recent years, and gives some discussion on the development of this field.
Path Planning for Underwater Robot Based on Improved Artificial Potential Field

Artificial potential field is often used for path planning of underwater robots. The artificial potential field is improved for solving the problems of unreachable target and local minimum in this paper. The influence of the ocean current is introduced, and the heading of the underwater robot under the action of the ocean current is deduced. Finally, the feasibility of the improvement is verified by simulation.
Deep CNN-based projection superimposition and structured light measurement experiment

To improve the resolution of the digital projection system, a low-resolution image projection superimposition method combining software algorithm and hardware design has achieved better results at a lower cost. This paper reports a dual-channel projection superimposed image decomposition network named PSSGNet based on deep CNN. PSSGNet combines batch renormalization, residual connection, and dilated convolution with shallow network depth and lightweight network parameters to generate two subframe images and synthesize superimposed images. Furthermore, we conduct sinusoidal structured light 3D measurement experiments for comparison using high-resolution images, superimposed images, and low-resolution images. Similar results for the first two show the potential of using the projection superimposed results of PSSGNet in industrial measurements.
Ink Jet Coding Quality Inspection System Design Based on YOLOX and CRNN

Typically, businesses that produce food use specialized inkjet printing equipment. Ink-jet printing machines frequently have flaws like missing codes, error codes, and shift codes because of mechanical or outside, uncontrollable factors. Manual detection is typically used in the traditional ink-jet code detection process, but this method is ineffective, labor-intensive for employees, and the detection accuracy depends too heavily on manual detection. This leads to the proposal of a character quality inspection algorithm based on YOLOX and CRNN. By changing the network structure, adding the CBAM attention mechanism, enhancing the loss function, and including a rotation mechanism, the character detection algorithm based on the YOLOX network is improved. The enhanced YOLOX network model has a precision increase of 2.59 over the basic model, a model size reduction of 12MB, with an accuracy rate of 99.59%. A final recognition rate of 97.8% is achieved for the character regions detected by the enhanced YOLOX and CRNN network model.
Face Mask Classification based on Transfer Learning and MobileNet V2

Face mask image recognition can detect and monitor whether people wear the mask. Currently, the mask recognition model research mainly focuses on different mask detection systems. However, these methods have limited working datasets, do not give safety alerts, and do not work appropriately on masks. This paper aims to use the face mask recognition detection model in public places to monitor the people who do not wear the mask or the wrong mask to reduce the spread of Covid-19. The mask detection model supports transfer learning and image classification. Specifically, the collected data are first collected and then divided into two parts: with_mask and without_mask. Then authors build, implement the model, and obtain accurate mask recognition models. This paper uses and size of images datasets tested respectively. The experimental results show that the effect of the image size of was relatively better, and the training accuracy of different MobileNetV2 models is about 95%. Our analysis demonstrates that MobileNetV2 can correctly classify Covid-19.
Robot Visual Localization Based on Adaptive Soft-Resampling Particle Filter Network

Particle filter is a powerful technique for sequential state estimation and has uses in several areas, including target tracking and robot localization. The particle filter network model for robot vision localization requires many particles for accurate localization in highly uncertain environments. We propose an Adaptive Soft-Resampling Particle Filter Network (ASRPF-Net) to solve this problem. The network mainly consists of a motion model, an observation model, and an adaptive soft-resampling model fused into an RNN unit in a recurrent neural network encoding a particle filter algorithm to learn the model for end-to-end temporal state estimation. Meanwhile, adding Semi-Supervised Learning to Adaptive Soft-Resampling Filter Network alleviates the need for labeled data and the problem that it may be costly for the model to obtain the actual state. Simulation experiments using a simulated environment in DeepMind Lab show that the proposed algorithm outperforms other models in global localization and tracking state estimation tasks when a large portion of the ground truth data is unknown. Moreover, the proposed algorithm further improves the particle degradation problem, thus increasing the accuracy and robustness of robot localization.
SERG: A Sequence-to-Sequence Model for Chinese ECG Report Generation

Medical report generation is a rising application of big data, which mitigates burden on doctors in clinical trail. However, the serious imbalanced distribution of diseases leads to data bias, an important issue in qualified medical reports generation. To address this issue, a sequence-to-sequence model with incorporation of clinical experience is proposed to generate Chinese ElectroCardioGraph (ECG) reports. The proposed model consists of Electrocardiograph Feature Extractor (EFE), Posterior Knowledge Embedding (PKE) and Report Generator (RG). Firstly, we introduce a novel spatial-temporal information fusion module in EFE to extract robust features from ECG data. Then, embeddings of ECG tags extracted from clinical ECG reports combined with output of EFE are then feed into PKE, which builds a bridge between ECG tags and ECG data, alleviating the problem caused by data bias. Finally, a transformer-based decoder is used in RG to generate ECG reports step by step with output of PKE as key and value. Experiments conducted on private data show that the proposed model can obtain an accuracy 50.26% on BLEU-4, 7.69% higher than state-of-the-art. Our method can also achieve better fluent reports, as demonstrated by the performance on CIDEr, a commonly used content metric.
Adjustable trajectory Planning of Robot under continuous acceleration constraint

Aiming at the problem that the solution obtained by the existing trajectory planning algorithm is not the optimal solution in the global space and the offline trajectory is formed, an adjustable trajectory planning algorithm under the continuous constraint of acceleration is proposed. The idea of binary search and numerical integration is used to achieve continuous path acceleration, while retaining the completeness of the planning algorithm; on this basis, an efficient trajectory adjustment mechanism is established, and the upper boundary of the feasible region can be changed by adjusting the functional parameters in the mechanism, to output different trajectories. With the help of the omnidirectional mobile robot based on the active eccentric wheel, the comparison results fully verify the effectiveness and superiority of the proposed the trajectory planning algorithm.
Text-to-Image algorithm Based on Fusion Mechanism

A multi-step text-generated image algorithm based on feature fusion is proposed to solve the problems of image blurring and missing image details in text-generated images. To enhance the ability of single-channel text features to guide multi-channel image features, the migration of text features to image feature is implemented using a feature fusion module that enhances text features while refining the details of the generated images. The fusion module is executed alternately with the upsampling operation in the generator to enhance the frequency of text feature usage. Placing the generator and discriminator is three pairs achieves the goal of generating fuzzy images from text to clear large images from fuzzy images.The experimental data of the model on the CUB dataset show an improvement in the Inception Score as well as the Frechet Inception Distance score, and the comparative analysis of the generated images shows that the images generated by this method have rich detail texture and sharpness.
A Pointer Generator Network Chinese News Abstractive Summarization Model Based on Pre-trained Language Model

Aiming at the situation that the existing Seq2Seq model is prone to deviate from the original meaning, lose contextual semantic information, and fabricate facts when generating news Summarization. This paper, based on the improvement of pointer generation network model, proposes a hybrid pointer generation network model based on BART pre-training language model. Firstly, the BART pre-trained language model based on denoising encoder is used to extract deep semantic features, and these semantic features are fused with the original news text and input into the pointer generation network model with coverage mechanism. The Bi-LSTM model is used to encode, and then the LSTM model with attention mechanism is used to decode the semantic vector to get the summarization content. In the decoding process, pointer mechanism solves the problem of out-of-vocabulary (OOV), coverage mechanism alleviates the problem of generating repeated words, and Beam search is used to improve the accuracy of summarization generation. The experimental results on NLPCC2017 data set show that the BART-PGN summarization model proposed in this paper obtains a higher Rouge score, which confirming its effectiveness in the field of Chinese news summarization.
Research on handwritten score recognition algorithm of test papers based on improved convolution neural network

The traditional way of marking papers relies mainly on manual work, and its process includes: marking, awarding marks, statistics, sorting and other processes. Teachers not only need to spend a lot of time and energy to increase the marking workload, but also prone to score summary error phenomenon. Nowadays, handwritten numeral recognition is gradually applied to various fields, which plays a huge role in information processing. This paper first analyzes the characteristics of common algorithms and builds an improved convolutional neural network to improve its recognition accuracy. Let it carry out deep learning of the handwritten scores on the test paper, which can be used for automatic recognition of the handwritten scores on the test paper. In the marking process, teachers only need to complete the grading work, and then the computer automatically completes the entry, summary and ranking of the scores. In this way, teachers can reduce the workload of marking, so that teachers can put their time and energy into teaching work.
A New Algorithm of Path Planning Based on Dynamic Window Approach

Path planning for dynamic obstacles is an important issue faced by unmanned aerial vehicles. This paper proposes an new dynamic window approach. By improving the evaluation function and combining it with the prediction method of dynamic obstacles, the ameliorated dynamic window approach can make the robot avoid the dynamic obstacles moving at high speed. In addition, the algorithm is simulated and verified in this paper. Lastly the results prove its performance, and compared with the original dynamic window method, it has a good improvement in all aspects.
Target adversarial sample generation for malicious traffic classification model

In the face of attacks against sample, machine learning technique is weak, the current flow against malicious classification samples against attack algorithm is numerous, but the vast majority of algorithms only to a certain classification model for design, migration attack ability is low, and is targeted, the target structure against samples as long as can let classification model classification error, The target attack has a clear directivity, which can mislead the model to classify as the designated category of the attacker. Therefore, in real scenes, the target attack will often bring greater threat, so it has more realistic significance. Based on Generative Adversarial Network (GAN), this paper proposes a method of generating target black-box Adversarial samples with norm limited disturbances. The shadow classifier is used to fit multiple Network traffic classification models as the discriminator and training generator. Generating and adding a specific disturbance that does not change the attack characteristics to a certain type of original malicious traffic, thus generating an adversarial sample that can attack multiple malicious traffic at the same time and misclassify it into a specified category by the classification model. The adversarial sample has strong target and good mobility.
Single object tracking based on OSTrack and NAS head

Neural architecture search (NAS) has attracted much attention in recent years. Especially, Differentiable Architecture Search (DARTS) has become a mainstream NAS algorithm because of its differentiable architecture hyper-parameter and low time complexity, but few people apply it in the field of object tracking due to its high memory complexity. This paper redesigns the cell structure according to the characteristics of the target tracking head network and defines a simplified search space. We propose a NASHT model to replace the three head networks of OSTrack with the modified DARTS cell, which automatically searches for a target structure suitable for its task. We use two benchmarks, PTB-TIR and LSOTB-TIR, to train and evaluate the model. The accuracy of the proposed model reached 86.5% and the success rate reached 69.0% on the PTB-TIR benchmark. Compared with OSTrack, the accuracy has increased by 1.5%, and the success rate has a 0.8% improvement. The accuracy of the proposed model on the LSOTB-TIR reached 84.9% and the success rate was 71.4%, which achieved comparable effects to OSTrack while performing better in situations such as motion blur, low resolution, and aspect ratio changes.
People Counting in Videos Based on Machine Learning

People counting plays a crucial role in various senses, such as concerts, scenic spots, etc. In this paper, we propose a people counting method applied in public areas. We combine Histogram of Oriented Gradient feature extraction and Support Vector Machine classification and applied them to crowd counting. Firstly, images are captured by the camera and HOG feature extraction is applied to generate gradient histograms. Then, Support Vector Machine is used for feature classification and after that, detection frames are generated around each person detected. Finally, number of frames is calculated. The method that combines HOG feature extraction and SVM classification is easy to achieve. Experiments show that it is fast and reaches high accuracy in most occasions.