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FastVGG network and its application in automatic identification of traffic signs

International Journal of Information and Communication technology 2022年第2期20卷 192-203页

作者： Gui, Kaige Gao, Shangbing Li, Xinyuan Lu, Zhonghe College of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China Jiangsu Mobile Internet of Things Technology Engineering Laboratory Huaian223001 China

Traffic sign recognition is an important part of driver assistance systems. Because the types of traffic signs are complex and diverse, they are difficult to identify. Traditional recognition methods require manual extraction of features, which is difficult and inaccurate. This paper proposes a FastVGG network based on VGG neural network to extract the features of the target image to realise the recognition of traffic signs under different angles and illumination. In the method of this paper, the connection layer parameters and the number of network layers are moderately reduced, the merging step is increased, and the recognition speed is improved. When the parameter value is less than zero, the leaky ReLU is used to replace the activation function to solve the problem of neuron death. The experimental results of the German traffic sign recognition dataset (GTSRD) show that the algorithm can achieve accurate classification while increasing the speed. © 2022 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Traffic signs

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Based on FCOS and HSV Recognition Algorithms for Oil Level and Implementations of Train Hydraulic Brake 2

Based on FCOS and HSV Recognition Algorithms for Oil Level a...

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2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022

作者： Chen, Qiuyang Pan, Kaixin Zhao, Jianyang Ding, Weihong School of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (数字)9781665454766

ISBN: (纸本)9781665454766

An important application in the field of Digital Image Processing is reading water level recognition. Currently, the automatic recognition method of train hydraulic brake oil level is uncommon, mostly by eye observation oil level position which can cause much errors and poor efficiency. Our project is about how to realize the automatic recognition of train hydraulic brake oil level reading. In this regard, we propose a model algorithm based on FCOS and HSV algorithm. © 2022 IEEE.

关键词： Water levels

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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ORFPPrediction: Machine Learning Based Online Recruitment Fraud Probability Prediction

ORFPPrediction: Machine Learning Based Online Recruitment Fr...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Zhang, Haiyan Wang, Meifeng Wang, Yuanyuan Li, Yazhou Gu, Dingyun Zhu, Yemeng Huaiyin Institute of Technology Institute of Computer and Software Engineering Jiangsu Province Internet of Things Mobile Interconnection Technology Engineering Laboratory Huai'an City Jiangsu223001 China

ISBN: (纸本)9798350306293

In response to the issue of inadequate public datasets concerning imbalances in Internet recruitment scams, this paper constructs an imbalanced experimental dataset, labels it, and proposes an ensemble learning method based on model stacking. This method enhances the prediction accuracy of the original model built with Random Forest. Initially, the method employs label encoding and TF-IDF (Term Frequency- Inverse Document Frequency) to preprocess structured and unstructured data respectively. Then, it utilizes Truncated SVD (Singular Value Decomposition) to reduce the dimensionality of the sparse matrix generated by feature extraction from TF-IDF. Subsequently, decision trees, random forests, and Light Gradient Boosting Machine are utilized as base models, with the random forest employed as the meta-model to integrate the predictions of the base models. This paper presents a comparison of the experimental results from seven machine learning models based on the self-constructed experimental dataset, with model stacking implemented on the random forest that yielded the best experimental result. Through model stacking enhancement of the machine learning models, compared to the random forest model that performed best on the self-constructed experimental dataset, an accuracy of 94.85% was achieved, marking a 7.33% improvement. The experimental results hold significant value and practical implications in understanding and resolving the probability risk issue of recruitment fraud. © 2023 IEEE.

关键词： Learning systems

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A privacy and compliance in regulated anonymous payment system based on blockchain

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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Research on web user's behaviour data mining based on feature orientation

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International Journal of Information and Communication technology 2021年第3期18卷 304-316页

作者： Zhang, Hui Jiang, Xiaoling Zhang, Fa Department of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China

In view of the problems of long mining time and high error rate in the existing network user behaviour data mining methods, a network user behaviour data mining method based on feature preference is proposed. The interaction relationship in social network is analysed as the constraint condition of feature selection. Laplasian operator is used to construct the feature selection model of network user correlation and to quantify the relationship between users. The improved ant colony algorithm is used to obtain the optimal feature subset to realise the network user behaviour data mining. The experimental results show that, compared with the traditional methods, the mining time of the proposed method is shorter and the mining error rate is lower under the condition of low and high excavation strength, which verifies the effectiveness of the proposed method. © 2021 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Data mining

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Scene graph generation based on global embedding and contextual fusion 3

Scene graph generation based on global embedding and context...

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3rd International Conference on computer Science and Communication technology, ICCSCT 2022

作者： Zhao, Zhiyong Hu, Ronglin Ma, Hongtai Zhang, Xinxin School of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huaian223003 China

ISBN: (纸本)9781510661240

As a structured abstraction method for objects and their interactions in visual scene, scene graph captures entities in the scene and the relationships between the entity pairs, and helps in understanding the visual scene better. Currently, scene graphs in most research works are generated by modeling using context information among targets, focusing only on the inference process but ignoring the integrity of the input target information and the impact of the global information of the targets on relationship inference. Therefore, a new scene graph generation method based on global embedding and contextual fusion (GECF) is proposed in this paper. In this method, richer entity information is obtained by embedding global information into the entity features, while more robust inference of entity interaction information and more reasonable relationship fusion are acquired by combining the attention weighting module and the context inference module as a joint inference module, and merging the obtained entity features according to their discrepancy. The experiment on Visual Genome dataset shows that GECF method performs better than the existing methods in scene graph visual relationship detection. © 2022 SPIE.

关键词： Embeddings

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Research on Residual Value Prediction of New Energy Second-hand Cars based on Prophet Multivariate Time Series Model

Research on Residual Value Prediction of New Energy Second-h...

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2022 International Conference on computer, Artificial Intelligence, and Control engineering, CAICE 2022

作者： Shuhao, Sun Hong, Zhou Junjie, Ji Siye, Liu Xiangyu College of Huaiyin Institute of Technology Jiangsu Huai'an China Computer and Software Engineering College of Huaiyin Institute of Technology Jiangsu Huai'an China

ISBN: (纸本)9781510655942

Nowadays, the rapid development of new energy vehicles has also driven the new energy second-hand car market. This study aims to solve the current problems of messy residual value evaluation of new energy second-hand cars, and better predict the residual value through the loss variables. It crawled the data from the second-hand car website through crawlers and found that new energy second-hand cars have trends, periodicity, and some additional effects. Therefore, this study adopted the Prophet Multivariate Time Series model for prediction whose input is the licensing time and kilometers, and output is the predicted price. Through trend comparison and accuracy evaluation, the model showed better prediction performance whose prediction accuracy of the residual value of new energy second-hand vehicles can reach 96%. © 2022 SPIE.

关键词： Value engineering

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An Improved NGO Algorithm for Optimizing VMD-CNN-BILSTM-Cross Attention Bearing Fault Diagnosis Method

An Improved NGO Algorithm for Optimizing VMD-CNN-BILSTM-Cros...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Jiapeng Luo Xiaobing Chen Faculty of Computer and Software Engineering Huaiyin Insititute of Technology Huai’an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Precisely identifying the fault-related operating state of the gear-box bearing represents a vital problem within industrial production. A bearing fault diagnosis method based on effectively improved NGO algorithm optimized VMD and CNN-BILSTM-Cross Attention is proposed for bearing fault diagnosis. The method improves the NGO algorithm by introducing the SMP chaotic mapping to initialize the eagle swarm distribution, introducing the sine function and the inverse learning strategy to enhance the algorithm’s computational precision and search ability. The CNN-BILSTM model is integrated with the Cross Attention module, which aims to merge the bearing signal features in multiple ways and boost the model’s accuracy and robustness. The improved SCNGO algorithm is utilized to iteratively optimize crucial parameters, namely the intrinsic mode coefficient, and the quadratic penalty term [K, α]within the VMD denoising technology. The CNN is responsible for seizing the spatial features of the bearing vibration signal at diverse frequencies, while the BILSTM is in charge of capturing the temporal features of the same signal. Moreover, the Cross Attention mechanism is introduced to blend the spatial features extracted by the CNN and the temporal features obtained by the BILSTM. In comparison to other models, the proposed model demonstrates stronger generalization and robustness.

关键词： Fault diagnosis Vibrations Accuracy Chaotic mapping Computational modeling Switched mode power supplies Feature extraction Data models Robustness Optimization

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