bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large Eddy Simulation of HAWT and VAWT performances in the vicinity of a building
Authors: Tene Hedje P. , Zeoli S. , Vigny U. , Houtin-Mongrolle F. , Benard P. , Bricteux L. 
Published: June 2022

AbstractThis work compares the performances of a Horizontal Axis Wind Turbine (HAWT) and a Vertical Axis Wind Turbine (VAWT) using Wall modeled Large Eddy Simulation (WMLES) coupled with an actuator line method. The wind turbines are located in the vicinity of a real size industrial building. Both wind turbines are sized to produce the same power at their respective optimum Tip Speed Ratio for a same incident wind speed. Two relevant incident wind directions (SW and SSW) are investigated, the influence of the building on the performance of the two wind turbines is also analysed. The results obtained show that the HAWT has a better overall performance compared to the VAWT. Overspeeds are observed for both directions analysed, due to the presence of the building which locally increases the flow velocity. However, these overspeeds remain low due to the low height of the building. The change of wind direction only slightly impacts the HAWT production, while the VAWT production remains insensitive. However, the presence of the building improves the global production of both wind turbines. Qualitatively, this change of wind direction induces a deviation in the wake of both turbines, which is greater for a SW direction.
Tensor Networks Meet Neural Networks
Authors: Xu Zenglin 
Published: June 2022

Abstract
As a simulation of the human cognitive system, deep neural networks have achieved great success in many machine learning tasks and are the main driving force of the current development of artificial intelligence. On the other hand, tensor networks as an approximation of quantum many-body systems in quantum physics are applied to quantum physics, statistical physics, quantum chemistry and machine learning. This talk will first give a brief introduction to neural networks and tensor networks, and then discuss the cross-field research between deep neural networks and tensor networks, such as network compression and knowledge fusion, including our recent work on tensor neural networks. Finally, this talk will also discuss the connection to quantum machine learning.
Research on Path Planning of Mobile Robot in Static Environment Based on Improved Ant Colony Algorithm
Authors: Sun Yisong , Bao Changchun , Li Le , Geng Zhenglin 
Published: June 2022

Abstract
In order to solve the problems of ant colony algorithm in the path planning of mobile robots, such as slow convergence speed and easy to fall into local optimum, an improved algorithm of adaptive ant colony algorithm is proposed in this paper. The algorithm improves the ant’s transition probability function by adding the number of revolutions and the angle of rotation to the heuristic function, and adding the target guide element, which can make the algorithm converge faster. The pheromone update formula is based on the wolfs food competition and distribution principle, which adaptively updates the pheromone volatility that satisfies the Cauchy distribution to avoid the local optimal solution found by the algorithm. Finally, by comparing and analyzing the statistical data results obtained by simulation, it can be concluded that the improved ant colony algorithm in this paper is better.
Road Roughness Recognition Based on Lidar
Authors: Chen Kai 
Published: June 2022

Abstract
The ups and downs of the road will have a great impact on the driving performance of the vehicle. Identify the road surface information in front of the vehicle in advance, and transmit this information to the vehicle’s control system. The control system can carry out targeted control according to the road conditions. Since the information on the road surface of the vehicle is mastered, the driving performance of the vehicle can be greatly improved. Firstly, a vehicle road recognition system is constructed using lidar as an environmental perception sensor to scan the road information ahead of the vehicle. Secondly, use GPS and inertial navigation system to monitor the vehicle’s driving position and attitude information, perform motion compensation on the lidar point cloud data, and extract the road point cloud. Thirdly, the elevation information of the road in front of the vehicle is obtained through filtering and interpolation of the point cloud. Finally, the error analysis of this method is carried out. The results show that the road elevation recognition error is within an acceptable range.
RA-ViT:Patch-wise Radially-Accumulate Module for ViT in Hyperspectral Image Classification
Authors: Wu Yuyang , Weng Qian , Lin Jiawen , Jian Cairen 
Published: June 2022

Abstract
Hyperspectral Images(HSIs) are data containing abundant spatial and spectral information, which is collected by advanced remote sensors. HSI Classification is a pixel-wise classification task that has broad prospects in the era of science and technology. In recent years, the widely used convolutional neural networks (CNNs) have come to the leading place in HSI Classification. However, the lack of utilization of spatial information limits its further application. To solve this issue, we considered the recently proposed Vision Transformer(ViT), which is modularized structures that are entirely based on self-attention mechanism. Furthermore, we proposed a patch-wise radially-accumulate module for ViT(RA-ViT) in HSI Classification. We evaluated the proposed method on Indian Pines(IP) and Kennedy Space Center(KSC) datasets. The results of experiments demonstrate the effectiveness of RA-ViT with comparison to current advanced models.
Design and Achieve of Management System for Optimal Allocation of Municipal Resources
Authors: Fan Lin , Wang Wei , Liu Qian , Li Tong-han 
Published: June 2022

Abstract
In order to achieve the informational and intelligent management of the municipal resources, this article uses platforms like IntelJ IDEA development tools and MySQL database, with the use of spring framework and B/S architecture, develops a municipal resource optimization configuration management system. In this system, MyBaits finishes the management of the data. The grey models and exponential smoothing algorithms have done the prediction part of the demand for the municipal resources corresponding to the flow of people and vehicles. The municipal department can perceive the demand for resources through this forecasting function and then allocate the corresponding municipal resources in advance. The development of this system can simplify the work process of municipal departments when there is a demand to manage the resources. It is of great significance to improve the working efficiency and the resources utilization rate of the municipal departments.
Research on Key Technologies of Street View Video Privacy Information Processing
Authors: Zhu Qianqian , Liu Yingnan , Zhao Zihao 
Published: June 2022

Abstract
As image and video data are recorded, stored or applied worldwide, data privacy protection is receiving increasing attention. Before data application, it is necessary to hide the information that can be personally identified in the video, for example, the human face that can be directly located to the personal identity, the license plate number that can be indirectly located to the personal identity and other data, while retaining the appropriate degree of data availability. Taking the actually collected European road street view video as an example, and taking the face and license plate as the privacy information, this paper proposes a method to automatically detect the face and license plate area in the street view video based on the YOLO5s model, and hide the detected object area with the Gaussian blur in the image processing technology. The experiment shows that the method is scientific, efficient and feasible.
Image Recognition With Haar Wavelet and Pseudoinverse Learning Algorithm Based Autoencoders
Authors: Feng Qingshuang , Yin Qian , Guo Ping 
Published: June 2022

Abstract
In this work, a recognition model with low computation and high training efficiency is proposed for images recognition. The proposed model consists of two modules: 1) haar wavelet transformation extracts features and compresses images; 2) a representation learning module using autoencoder. Because of its powerful function, Haar wavelet transformation has wide use in the feature extraction of images. The mother wavelet and scale wavelet can extract features from different scales. The representation learning module is trained with a non-gradient descent algorithm based on autoencoder structure. Two benchmark image datasets, MNIST and Fashion-MNIST, have been used to validate the proposed model. Experimental results show that our proposed model achieves its expected effect. Compared with filters, CNN, and pseudoinverse learning autoencoders, our model takes less training time, at the same time it acquires comparative recognition accuracy.
Unsupervised Change Detection in Hyperspectral Images using Principal Components Space Data Clustering
Authors: Li Yinhe , Ren Jinchang , Yan Yijun , Liu Qiaoyuan , Petrovski Andrei , McCall John 
Published: June 2022

Abstract
Change detection of hyperspectral images is a very important subject in the field of remote sensing application. Due to the large number of bands and the high correlation between adjacent bands in the hyperspectral image cube, information redundancy is a big problem, which increases the computational complexity and brings negative factor to detection performance. To address this problem, the principal component analysis (PCA) has been widely used for dimension reduction. It has the capability of projecting the original multi-dimensional hyperspectral data into new eigenvector space which allows it to extract light but representative information. The difference image of the PCA components is obtained by subtracting the two dimensionality-reduced images, on which the change detection is considered as a binary classification problem. The first several principal components of each pixel are taken as a feature vector for data classification using k-means clustering with k=2, where the two classes are changed pixels and unchanged pixels, respectively. The centroids of two clusters are determined by iteratively finding the minimum Euclidean distance between pixel’s eigenvectors. Experiments on two publicly available datasets have been carried out and evaluated by overall accuracy. The results have validated the efficacy and efficiency of the proposed approach.
The Application of Particle Swarm Optimization in Neural Networks
Authors: Gao Lizhen , Lin Yanfen 
Published: June 2022

Abstract
The birth of the neural network model has provided a technological platform for the development of various industries. Among them, the branch network scientific research management model represented by the scientific research field is particularly profitable. China’s traditional research management system has been unable to meet the requirements of the present era and the management system reform is imperative. Therefore, based on Particle Swarm Optimization (PSO), this dissertation studies the neural network model based on the prerequisite that the branch network research management system retains the advantages of the traditional model. It tracks the optimal value of current search through cross and mutation. The optimal solution value is finally determined by solving the relationship between particle weight and inertia weight. In the final test, we establish the feasibility of the program.
Research on Continuous Static Coverage of Multiple Heterogeneous Warning Vehicles Considering No-fly Areas
Authors: Duan Xiaowen , Bo Li 
Published: June 2022

Abstract
Through the analysis of the types of tasks and flight routes faced by the warning vehicle, this paper proposes the problem of continuous static coverage of the multi-heterogeneous warning vehicle (referred to as WV) considering No-fly areas to be solved. By analysing things like detection area, no-fly area, WV patrol strategy form, patrol strategy planning mechanism and detection capability of WV, based on the philosophy of model stratification and time segmentation, a multi-layer multi-stage patrol strategy planning method (referred to as MLMPSPM) for determining the patrol strategies of multiple heterogeneous warning vehicles (WVs) during multi-patrol cycles considering no-fly areas is proposed, including the process of determining the planning stage index. In detail, MLMPSPM mainly introduces the patrol strategy planning method of
EF
and
ER
. Whether
EF
or
ER
includes input section, planning section, stage warning effect of planning stage, and patrol strategy objective planning, etc.
EU
planning processes is the extension of
EF
and
ER
. In the experimental results section, the relevant experimental parameters are designed in detail, the experimental results show that MLMPSPM can not only determine the patrol strategies of all WVs throughout the alert process considering time-varying no-fly areas, but also achieve a large total warning effect.
Conformity Assessment of Informative Labels in Car Engine Compartment with Deep Learning Models
Authors: Ferreira R , Barroso J , Filipe V 
Published: June 2022

Abstract
Industry 4.0 has been changing and improving the manufacturing processes. To embrace these changes, factories must keep up to date with all the new emerging technologies. In the automotive industry, the growing demand for customization and constant car model changes leads to an inevitable grow of complexity of the final product quality inspection process. In the project INDTECH 4.0, smart technologies are being explored in an automotive factory assembly line to automate the vehicle quality control, which still relies on human inspection based on paper conformity checklists. This paper proposes an automated inspection process based on computer vision to assist operators in the conformity assessment of informative labels affixed inside the engine compartment of the car. Two of the most recent object detection algorithms: YOLOv5 and YOLOX are evaluated for the identification of labels in the images. Our results show high mean average precision on both algorithms (98%), which overall, tells us that both algorithms showed good performances and have potential to be implemented in the shop floor to support the vehicle quality control.
A Compact Object Detection Architecture with Transformer Enhancing
Authors: Cui Liyuan , Zhong Guoqiang , Liu Xiang , Xu Hongwei 
Published: June 2022

Abstract
With the advancements in rising computer vision processing, Transformer has attracted increasing interesting in this field. However, it is limited because of its unprecedented storage, heavy reliance on data size and intolerable computational power consumption. While lightweight network is in other extreme, pursuing the compact architectures accompanied by performance loss. In this paper, we enhance an architecture as the backbone of object detection networks through combining right-size Transformer, i.e. Vision Transformer module. Specifically, based on GhostNet, a well-known lightweight neural network structure moreover, embed this Vision Transformer module at the end of GhostNet, and use the input data with slicing design to reduce the computational burden of the neural networks. Vision Transformer is taken to enhance the architecture as the backbone of object detection networks, and the well-known YOLOv5 as the baseline. We conduct multi-metric comparison experiments on two medium-scale object detection datasets with large, medium and small scale networks. Results show that without relying on ultra-large dataset and pre-trained models, the proposed Transformer module enhanced architecture achieves comparable or even higher mAP metrics with only half of the model size and floating-point computation of the baseline.
Rotate-Yolov5 for Aerial Images
Authors: Chen H , Liu F X , Huang X L 
Published: June 2022

Abstract
In Recent years, great progress has made in object detection. However, since the orientation of object in aerial image is random, the regular horizontal object detection method is not suitable for aerial images. In this paper, we present a Rotate-Yolov5 network based on Yolov5. We use an Adaptive Rotating Anchor Generation Module (ARAGM) to generate anchors with object orientation information. Then the orientation information is used for Rotate-Deformable Convolution Module (R-DCM) to extract features. Finally, we use a decouple detection head as Oriented Object Detection Module (OODM) to yield classification and regression results. Moreover, Rotate-Smooth L1 is used to optimize the loss function. We evaluate the proposed Rotate-Yolov5 on DOTA datasets and the mAP reached 75.4, which demonstrate the superiority of its effectiveness.
Research on License Plate Location Algorithm Based on YOLOv5
Authors: Zhu Qianqian , Liu Yingnan , Zhao Zihao , Ma Xin 
Published: June 2022

Abstract
License plate location is a key part of license plate recognition, and its positioning accuracy seriously determines the final result of license plate recognition. Firstly, the traditional license plate location methods are compared and analyzed. Secondly, in order to solve the localization problem in low resolution and multi-vehicle environment, a license plate method based on YOLOv5s was proposed by using deep learning image recognition technology, and data enhancement was introduced to improve YOLOv5s. Finally, the YOLOv5 algorithm is applied to real vehicle images under complex environmental conditions for experimental verification, and the results show that the accuracy is 99.12%. The experimental results show that the license plate location model based on YOLOv5S can effectively solve the influence of illumination, image quality and other factors on license plate location, improve the efficiency and accuracy compared with the traditional license plate location method, the algorithm has good robustness and fast calculation speed.
Face Shape Classification Based on Bilinear Network with Attention Mechanism
Authors: Duan Jiawei , Su Xueping , Ren Jie , Xie Longsheng 
Published: June 2022

Abstract
Face shape is an important information in face recognition, personalized recommendation and other applications. For example, rough face shape filtering before face recognition can effectively improve the recognition accuracy and speed. At the same time, an effective face shape classification can be used to construct a recommendation system for hairstyles and glasses. Therefore, this paper proposes a new face shape classification algorithm. Firstly, the M-RetinaFace network is proposed to align face image. Secondly, by combing the attention mechanism with the EfficientNet bilinear network, the AB-CNN network is proposed to extract feature. Finally, a bilinear pooling layer is used to classify face shape. Experiments on public data sets show that, compared with existing algorithms, the algorithm proposed in this paper is get state-of-the-art results and significantly improves the accuracy of face shape classification.
Brain Tumor Image Segmentation Based on Grouped Convolution
Authors: Wu Mingliang , Ye Hai-Li , Wu Yun , Li Jianmin 
Published: June 2022

Abstract
The automatic segmentation of MRI multi-modal images of brain tumors is one of the important research contents of disease detection and analysis. Due to the heterogeneity of tumors, it is difficult to achieve efficient and accurate automatic segmentation of brain tumors. Traditional segmentation methods based on machine learning cannot handle complex scenes such as complex edges and overlapping categories. In clinical assisted diagnosis, it is of great significance to apply deep learning to two-dimensional natural image segmentation and three-dimensional medical image segmentation. In this paper, we propose a three-dimensional network model to achieve precise segmentation of brain tumors. The model adopts an encoder-decoder structure and replaces ordinary convolution with grouped convolution to reduce network parameters and improve network performance. The model improves the problem of information exchange between different groups through channel mixing. Experiments conducted on the BraTS (Brain Tumor Segmentation) 2018 challenge dataset prove that our network greatly reduces the computational cost while ensuring segmentation accuracy.
An Automatic Detection Method for COVID-19 in CT Images
Authors: Li Yarong , Jiang Yizhang , Gu Yi , Qian Pengjiang 
Published: June 2022

Abstract
There are many new cases of new coronary pneumonia (named COVID-19) every day around the world. In such a severe situation, effective detection of COVID-19 has become extremely important. Studies have shown that chest CT images can be used for COVID-19 detection because they can show bilateral changes in the lungs of people infected with COVID-19. It is not difficult for experienced radiologists to make preliminary judgments based on CT images. However, with the emergence of a large number of suspected cases, the explosive demand has overwhelmed doctors. Therefore, the automatic diagnosis of COVID-19 CT images is of great significance to realize early diagnosis, early isolation, and early treatment. In this paper, we study an automatic identification method that uses transfer learning technology, which transfers the pre-trained VGG16 network for feature extraction, and combines VAE augmentation data to reduce over-fitting, and finally uses integrated technology to achieve better detection results. Our experiments have shown that the accuracy of our method for identifying whether a patient’s CT image is positive or negative for COVID-19 is 91%, the precision is 88%, the recall rate is 94%, and the F1-score is 91%. Compared with other the state of art methods, the method proposed in this article can provide more efficient classification and identification of COVID-19.
Development of agarose–gelatin bioinks for extrusion-based bioprinting and cell encapsulation
Authors: Dravid Anusha , McCaughey-Chapman Amy , Raos Brad , O’Carroll Simon J , Connor Bronwen , Svirskis Darren 
Published: June 2022

Abstract
Three-dimensional bioprinting continues to advance as an attractive biofabrication technique to employ cell-laden hydrogel scaffolds in the creation of precise, user-defined constructs that can recapitulate the native tissue environment. Development and characterisation of new bioinks to expand the existing library helps to open avenues that can support a diversity of tissue engineering purposes and fulfil requirements in terms of both printability and supporting cell attachment. In this paper, we report the development and characterisation of agarose–gelatin (AG–Gel) hydrogel blends as a bioink for extrusion-based bioprinting. Four different AG–Gel hydrogel blend formulations with varying gelatin concentration were systematically characterised to evaluate suitability as a potential bioink for extrusion-based bioprinting. Additionally, autoclave and filter sterilisation methods were compared to evaluate their effect on bioink properties. Finally, the ability of the AG–Gel bioink to support cell viability and culture after printing was evaluated using SH-SY5Y cells encapsulated in bioprinted droplets of the AG–Gel. All bioink formulations demonstrate rheological, mechanical and swelling properties suitable for bioprinting and cell encapsulation. Autoclave sterilisation significantly affected the rheological properties of the AG–Gel bioinks compared to filter sterilisation. SH-SY5Y cells printed and differentiated into neuronal-like cells using the developed AG–Gel bioinks demonstrated high viability (>90%) after 23 d in culture. This study demonstrates the properties of AG–Gel as a printable and biocompatible material applicable for use as a bioink.