bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using VoteNet to detect beverage bottles
Authors: Ji Haocheng 
Published: December 2023

Abstract
When using point cloud to detect indoor objects, the collected point cloud often has a lot of noise, such as points that are too far away, background points that have nothing to do with the objects to be detected, which will have a bad impact on the detection. In order to solve these problems, a method is proposed to preprocess the collected original point cloud before training and prediction. First, the points that are too far away should be eliminated. Second, the background such as the table should be eliminated as much as possible. Then, the coordinate conversion is required for the point cloud. Finally, VoteNet is used to train these processed point clouds to obtain the model, which is then used for prediction. The experimental results show that the model is good when using these processed point cloud data for training and prediction. In addition, this paper proposes to obtain point cloud by scanning objects, and then process the obtained point cloud to expand the dataset. The significance of this study is using the collected point cloud data for final training and prediction and expanding the dataset without using 3D cameras.
Virtual flight-based design and simulation of a quadrotor UAV
Authors: Guo Haoxu 
Published: December 2023

Abstract
Lately, Quadrotor UAVs control the attitude and position of the vehicle by adjusting four motor speeds to vary the rotor speed and achieve changes in lift. A quadrotor UAV is a six-degree-of-freedom vertical elevator. As drone technology matures, the demand for drones is increasing and virtual drone control technology offers new ways to skillfully control drone flight techniques, with multiple scenarios virtually minimising the variety of situations encountered in real-world situations. At the same time, the gradual maturation of 5G technology opens up even more possibilities for how drones can be controlled.
Learning to self-adaptive robust dehazing network for video detection in drainage pipeline
Authors: Qiao Jingyi , Li Ce , Tang Zhengyan , Huang Yingjie 
Published: December 2023

Abstract
With the continuous urbanization and expansion of the built-up area of cities, the requirement for regular maintenance of urban underground drainage pipeline will be further increased. However, the drainage pipeline environment is humid and the water quality conditions are complex, which leads to the existence of uneven concentration of haze in the images captured by CCTV (closed-circuit television). To address these problems, this paper suggests a self-adaptive robust dehazing network for video detection in drainage pipeline. The method extracts feature in the encoder by self-adaptive module and fully multi-scale feature alignment module, and the dehazed image is gradually recovered in the enhanced decoder after the feature recovery module. The results of the experiments indicate that this method significantly improves the robustness and generalization ability of model dehazing by capturing global hazy features at multi-scale resolution, and it is an effective method for applying to drainage pipeline dehazing.
SewerOD: A visual sewer disease detection dataset for machine learning
Authors: Wei Wei , Li Ce , Li Shuo , Chen Zheng , Yang Feng 
Published: December 2023

Abstract
The underground sewer pipeline is an essential urban infrastructure that undertakes the vital responsibility of discharging sewage. The location and type of diseases in the pipeline often need to be checked manually by inspectors, which cannot be completed efficiently due to labor costs and time requirements. With the development of computer vision, the use of detection technology to maintain sewer pipelines has extremely high research value. However, image data of pipelines are often regarded as commercial secrets, and these studies are greatly limited due to the scarcity of open-source pipeline disease datasets. To solve this problem, we present a public large-scale object detection dataset for sewer disease detection named SewerOD in this work. The dataset contains about 47K images, annotated by professional researchers, and includes two of the most widespread structural disease types: Corrosion and Crack. Our dataset is available at https://github.com/SewerOD.
Comparison of machine learning algorithms and feature visualization analysis for diabetes risk prediction
Authors: Chen Siyuan 
Published: December 2023

Abstract
There are already 537 million people suffering from diabetes worldwide, according to the research data released by the International Diabetes Federation in 2022. It is a frequent and pervasive condition that has overtaken cancer as the second leading cause of death from modern diseases and is second only to it in terms of the harm it causes to the human body. The excessive morbidity, mortality, shortened life expectancy, and economic and other costs associated with it make it an important condition that plagues people. This study selected the Diabetes Health Indicators Dataset from the Kaggle database based on machine learning algorithm prediction models in predicting the risk of diabetes. 70% of the data were randomly chosen from the presence of high blood pressure (High BP), high cholesterol (High Chol), body mass index (BMI), smoking status (Smoker), and the risk of diabetes, smoking status (Smoker), health status, age, education, income, and gender as independent variables, and whether or not they had diabetes as dependent variables, and built diabetes prediction models based on five traditional machine algorithms such as random forests, logistic regression, decision trees, Support Vector Machine (SVM), XGBoost, and deep neural networks (DNN). The models were then validated with 30% of the samples. Three indicators, Accuracy, AUC, and Recall, were compared and analyzed, and DNN was found to have a high prediction accuracy (75.49%) and recall rate (79%).
Expansion of the CT-scans image set based on the pretrained DCGAN for improving the performance of the CNN
Authors: Cheng Ruoxi 
Published: December 2023

Abstract
Diagnosis of COVID-19 based on CT scans images using artificial intelligence can massively reduce medical manpower, time, and resources and reduce misdiagnosis rate. However, there are few relevant image sets publicly available at present, which is not conducive to the training of models such as classification. Transfer learning has been extensively considered in image classification, but less utilized in image generation. This work attempts to expand this dataset with discriminators using Deep Convolutional Generative Adversarial Network (DCGAN) models of Imagenet pre-training models and verify the significance of pre-training models for generating image veracity. A DCGAN model using the Imagenet pre-training model (i.e. resnet18) and a DCGAN network without the pre-train model generated 100 positive and 100 negative images to extend the original dataset, respectively. These images were classified using a Convolutional Neural Network (CNN) binary classifier, and the accuracy improved from 82.67% to 85.33% after adding the pre-training model. Furthermore, the comparison shown by the Gradcam visualization shows that the discriminator with the pre-training model can better capture the key details of the images. This experiment shows that even though Imagenet and lung images are totally uncorrelated, there are still some features that can be transferred. It also demonstrates that Imagenet pre-training models can improve the quality of GAN-generated images, extending the application of migration transfer learning.
Research on signal processing of upper limbs of three gestures
Authors: Yang Rongkai , Jiang Jiajun , Jiang Zhaoling , Zhu Jiaming 
Published: December 2023

Abstract
There are a large number of disabled people in the world whose lives are seriously affected by the lack of upper limbs. Research on related prostheses is crucial to making these individuals’ lives as convenient as those of regular people. The association between the acts that took place and the relevant data waveforms are determined in this investigation. To lessen the impact of fluctuations in the data itself, EMG signals are obtained using the five channels’ worth of data. By examining the related EMG data, preprocessing, feature extraction, and the construction of two CNN models are used to categories three gestures (scissors, rock, and cloth). Finally, the classification accuracy and loss indicate that the 2-D CNN model is better by comparison with the 1-D CNN model in 3 gesture classification. These correlation results show that the control of the prosthetic limb can realize the completion of various actions through the analysis of the corresponding data and transmission to the entity.
End to end multifunctional autoencoder-based classification and denoising network
Authors: Zhou Mingze 
Published: December 2023

Abstract
Brain tumours are one of the common diseases in human beings. Currently, brain Nuclear Magnetic Resonance (MRI) is the main means of detecting brain diseases. There are many works related to the classification and noise reduction of brain MRI images, but there are few works that combine these two parts simultaneously, i.e., noise reduction processing and classification of brain images at the same time. In this work, these two parts of work will be combined together, extracting the features of brain MRI images through an encoder composed of convolutional neural networks, using the features to classify the images, and then using the features to reduce the noise of the images to produce a low-noise image. According to the model, this work also proposes a new loss function. The new loss function is composed by adjusting the weights of the loss functions related to the classification and denoising tasks. This paper tries three different ways of weight assignment, and the experimental results show that the dynamic parameter assignment approach achieves the best results for image classification, but none of the three approaches achieves acceptable results for noise reduction.
Traffic sign detection based on classic visual recognition models
Authors: Yang Jirui 
Published: December 2023

Abstract
This paper aims to assess the effectiveness of various object detection high-level architectures, including Faster R-CNN, R-FCN, SSD, and YOLO, in recognizing traffic signs. Since traffic sign recognition is a critical element of driver-assistance and autonomous driving systems, this study explores how five distinct architectures for feature extracting (ResNet V1 50, ResNet V1 101, Inception V2, Inception-ResNet-V2, and Darknet-19) can enhance the performance of these meta-architectures. To evaluate the models, the authors fine-tuned a pre-trained object recognition algorithm on the German Traffic Sign Detection Benchmark (GTSDB) dataset. While the Faster R-CNN Inception Resnet v2 proved to be the most accurate among the models, the unbalanced distribution of mandatory, prohibitory, and danger signs in the GTSDB dataset may have caused biased results in object detection. Overall, the result obtained from this study can help improve the accuracy of driver-assistance and autonomous driving systems by providing insights into the performance of various object detection models in recognizing traffic signs.
YOLO sparse training and model pruning for street view house numbers recognition
Authors: Zhang Ruohao , Lu Yijie , Song Zhengfei 
Published: December 2023

Abstract
This paper proposes a YOLO (You Only Look Once) sparse training and model pruning technique for recognizing house numbers in street view images. YOLO is a popular object detection algorithm that has achieved state-of-the-art performance in various computer vision tasks. However, its large model size and computational complexity limit its deployment on resource-constrained devices such as smartphones and embedded systems. To address this issue, we use a sparse training technique that trains YOLO with L1 norm regularization to encourage the network to learn sparse representations. This results in a significant reduction in the number of parameters and computation without sacrificing accuracy. Furthermore, we apply a model pruning technique to the sparse-trained model to reduce the model size and computation. We evaluate our proposed method on the SVHN (Street View House Numbers) dataset. We show that it performs comparably to the original YOLO model while reducing the model size by 5% and the computation time by 7%. Overall, our proposed YOLO sparse training and model pruning technique provides an effective solution for deploying YOLO-based object detection models on resource-constrained devices.
Performance analysis of parallel programming models for C++
Authors: Zeng Guang 
Published: December 2023

Abstract
As multicore processors become more common in today’s computing systems and parallel programming models are enriched, programmers must consider how to choose the appropriate and parallel programming model when writing parallel code. The purpose of this paper is to compare and analyze the performance gap between different C++ parallel programming models, such as C++ standard library threads, OpenMP and Pthreads, in terms of matrix operations. The experiments use different libraries to implement matrix multiplication separately and then analyze their performance. The experimental data show that the data size has a significant impact on the performance of the different models. For very small matrices of size magnitude less than or close to the number of threads, the performance of parallel implementations is much lower than that of serial implementations. For small matrices with magnitudes larger than the number of threads, the C++ standard library threads outperforms Pthreads and OpenMP due to its lightweight thread performance on relatively small matrices. pthreads shows the best performance on very large matrices due to its fine-grained control over thread management, communication, and synchronization operations. openMP’s is not as stable as the other two libraries, especially for smaller matrices. This paper provides a comparative analysis that can help programmers choose the most appropriate library for their specific computational needs.
Enhanced stability of aerial refueling process of UAV based on fuzzy PID
Authors: Liu Yuhang 
Published: December 2023

Abstract
This essay aims to study UAV(Unmanned Aerial Vehicle) aerial refueling technology, and by understanding the control analysis of aerial UAV refueling by comparing multiple R&D(Research And Development) teams, a more practical and feasible method is obtained, that is, through the discussion and design of UAV motion modeling, UAV trajectory tracking control system, fuzzy PID(proportion integral derivative) controller design, etc., the purpose of UAV aerial refueling is finally achieved, and the time of UAV is increased to enhance the stability and durability of UAV in air operation.
Prediction model of pet rescue outcome based on LightGBM and Bayesian optimisation
Authors: Chen Lin 
Published: December 2023

Abstract
Due to owner abandoning, pets getting lost, and irresponsible breeding, the number of strays is rising steadily as more individuals opt to have pets. Stray animals are not only in a precarious position, but they also wreak havoc on the lives of humans. To reduce the number of stray animals and enhance the pet rescue mechanism, this paper develops a prediction model of pet rescue outcome based on LightGBM (Light Gradient Boosting Machine). The model selects data features using the Pearson correlation coefficient, employs SMOTE (Synthetic Minority Oversampling Technique) to balance the data, and then uses LightGBM modelling to predict the outcome of each pet, such as adoption, transfer, death, etc. Simultaneously, the Bayesian algorithm is used to optimise the model’s hyper-parameters in order to generate the final model. Comparing the prediction results of SVM, Random Forest, and BP neural network models with those of the model based on LightGBM, the experimental results on the real animal centre dataset reveal that the model based on LightGBM has a superior predict performance, and the Bayesian optimisation effect is evident. In addition, the paper assesses the importance ranking of model features to aid animals in animal shelters more effectively.
Transformer-based visual question answering model comparison
Authors: He Zhicheng , Li Yuanzhi , Zhang Dingming 
Published: December 2023

Abstract
The potential use of visual question and answer (VQA) models in fields including robotics, autonomous driving, and health care has recently attracted a lot of attention. However, the development of the multi-modular learning VL (Vision-Language Learning) model itself in the “pre-training + fine-tuning” mode is challenged under the rapid development of large-scale language models. Therefore, optimizing the value and feasibility of the single-task model itself is also worth thinking more cautiously. With both model principles, structures, and experimental contrast analysed in this paper, the two excellent models, LXMERT and UNITER, are evaluated to demonstrate the reasons for performance differences on specific downstream tasks VQA deeply. In addition, this paper provides insight into the possibility of further model optimization in the future for multi-modular tasks, especially VQA. In comparison to UNITER, the findings reveal greater accuracy in the training set and verification set from beginning for LXMERT, which indicates that the better model architecture and pre-training method is where LXMERT’s higher adaptability probably comes from. However, the UNITER, which has fewer parameters and smaller architecture, can also achieve fine results after fine-tuning. This paper highlights the differences in accuracy shown by existing Transformers-based VQA models in local test sets and explores the possibility of optimizing VQA systems through fine-tuning.
Combining feature pyramid and CycleGAN for image generation
Authors: Li Yuqi , Lu Jiren , Meng Xiangyu 
Published: December 2023

Abstract
Image generation has always been a hot topic in computer vision community, which aims to learn the data distribution from a give image dataset and then generates new images obeying this distribution. Thanks to the rapid development of convolutional neural networks, breakthroughs have been made in the accuracy and speed of image generation. Currently, image generation is mainly based on the framework for generating countermeasures networks. However, limited by the quality of features, the generated images still have problems such as edge blurring, which restricts large-scale practical applications. In this paper, we propose an image generation algorithm that combines feature pyramid network (FPN) and CycleGAN. Specifically, the FPN containing two upsampling and feature fusion operations is added after the residual blocks and before the decoder of the CycleGAN generator, which can help the generator produce more detailed images and learn from features at different scales. Qualitative and quantitative experimental results show that the CycleGAN model with FPN outperforms the original CycleGAN model in terms of image generation.
Review on data imputation methods in machine learning
Authors: Xue Jianing 
Published: December 2023

Abstract
Data is an important element in the analysis of machine learning. It is usually measured based on observations and is also an indispensable element in training a model. Good preparation of data helps enhance the performance of analysis and is able to deliver reliable final results. However, lots of factors influence the dataset and some lead to the loss of some data. When some portion of the data is missing, it causes biases in the final prediction outcomes. In order to minimize the consequences of missing data, several data imputation methods are established to solve the problem. This paper will first talk about some basic concepts about missing data. In the following sections, the paper will present several popular data imputation methods, including complete case analysis, single imputation, and multiple imputations. Applications of some methods will be presented to see how they can be used in real analysis situations. Finally, the paper will talk about the limits of current data imputation methods.
Study on video key frame extraction in different scenes based on optical flow
Authors: Dong Jinshan 
Published: December 2023

Abstract
Key frame extraction is an important component of video analysis, and has gradually become a research hotspot in the computer vision community in recent years. Early key frame extraction algorithms were mostly based on fixed time intervals, and their accuracy could not meet practical application requirements. Thanks to the rapid development of machine learning technology, key frame extraction algorithms based on image quality, motion analysis, and deep learning are gradually becoming mature. Although the above methods significantly improve the accuracy of key frame extraction, few work pay attention to the extraction effect in different scenarios. In this article, selecting the key frame extraction algorithm based on optical flow method, we compared the key frame extraction algorithm based on optical flow method in detail for different video scenarios. Extensive experimental results demonstrate the robuteness of key frame extraction algorithm based on optical flow.
Automatic food recognition based on efficientnet and ResNet
Authors: Liu Yizhe 
Published: December 2023

Abstract
Automatic Food recognitionbased image is an emerging topic, which aims to extract the feature of given food image and then predict its category. Compared with the ordinary image recognition task, the food recognition is more challenging due to the high similarity between subcategories and the high inter-class variability of specific category. Early food image recognition methods are mainly based on manual features such as color, shape, texture and other information, which cannot meet actual application requirements limited by insufficient feature expression capabilities. Thanks to the rapid development of convolutional neural network, the methods based on deep learning boost the recognition accuracy and speed significantly. In this paper, we construct the food recognition models based on the EfficientNet and ResNet, respectively. Extensive experiments have been conducted on Food-101 dataset. All the results show the effectiveness of our models, which can recognize various foods accurately.
Improvements in adverse drug reaction prediction
Authors: Zhu Wanyu , Wang Yusen , Ma Yuzhou , Wang Peirui , Cui Tingyue 
Published: December 2023

Abstract
This report investigates prediction on adverse drug reactions (ADR) with kernel and imbalance data mechanisms. The hypothesis is that different types of kernel lead to different prediction results, which suggests deciding the best-fit kernel might be a critical way of improving prediction accuracy. Besides, it was also hypothesized that edge cases in real-life setting would cause imbalance in the dataset, thus further causing inaccuracy in prediction. Similarly, attempting to add class weight to various machine learning models could also be a way to improve prediction accuracy. Hence, these hypotheses are being explored in this study.