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Identification of Core Technological Topics in the New Energy Vehicle Industry: The SAO-BERTopic Topic Modeling Approach Based on Patent Text Mining 5

Identification of Core Technological Topics in the New Energ...

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Joint Workshop of the 5th Extraction and Evaluation of knowledge Entities from Scientific Documents and the 4th AI + Informetrics, EEKE-AII 2024

作者： Zhu, Jianxin Chuang, Yutong Wang, Zhinan Li, Yunke Harbin Engineering University School of Economics and Management 150001 China Key Laboratory of Big Data and Business Intelligence Technology Ministry of Industry and Information Technology China

In the new energy vehicle industry, precise identification of core technologies is the key to promoting innovation and maintaining market competitiveness. In this article, a comprehensive approach combining information weight method and SAO-BERTopic topic model is proposed to extract and analyze core technologies from large-scale patent data. Through in-depth analysis of Incopat patent database, we use the information weight method to select high-quality core patents from four dimensions: technological, strategic, law and market value. These selected patents form the basis of the research data, which is then applied to the SAO-BERTopic model, which combines the advanced semantic understanding capabilities of BERTopic with the fine-structured characteristics of SAO analysis, greatly improving the efficiency and accuracy of identifying complex technical topics. This innovation of this research is not only applicable to the analysis of the technological development of the new energy vehicle industry, but also can provide valuable reference for other high-tech industries such as biomedicine, renewable energy and information technology. These fields also need to identify core technologies from a large number of patents. SAO-BERTopic's structured analysis framework can help these industries to insight into technology development trends, identify innovation opportunities, and provide data-driven decision support for enterprises, research institutions and governments, thus playing an important role in technology planning and market commercialization. © Copyright 2024 for this paper by its authors.

关键词： Commerce

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Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

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Computers, Materials & Continua 2023年第4期75卷 565-586页

作者： Wenxing Zhang Jingbing Li Uzair Aslam Bhatti Jing Liu Junhua Zheng Yen-Wei Chen School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311100China Graduate School of Information Science and Engineering Ritsumeikan UniversityKusatsu525-8577Japan

The field of medical images has been rapidly evolving since the advent of the digital medical information ***,medical data is susceptible to leaks and hacks during *** paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage,as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking ***,a pre-trained GoogLeNet network is used in this paper,based on which the parameters of several previous layers of the network are fixed and the network is fine-tuned for the constructed medical dataset,so that the pre-trained network can further learn the deep convolutional features in the medical dataset,and then the trained network is used to extract the stable feature vectors of medical ***,a two-dimensional Henon chaos encryption technique,which is more sensitive to initial values,is used to encrypt multiple different types of watermarked private ***,the feature vector of the image is logically operated with the encrypted multiple watermark information,and the obtained key is stored in a third party,thus achieving zero watermark embedding and blind *** experimental results confirmthe robustness of the algorithm from the perspective ofmultiple types of watermarks,while also demonstrating the successful embedding ofmultiple watermarks for medical images,and show that the algorithm is more resistant to geometric attacks than some conventional watermarking algorithms.

关键词： Zero watermarks GoogLeNet medical image Henon map feature vector

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A multi-level FW-UAV fault diagnosis solution based on multi-task learning under small samples 14

A multi-level FW-UAV fault diagnosis solution based on multi...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Zhang, Yizong Yang, Lei An, Xue Li, Shaobo Zhang, Ansi Liao, Zihao School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (纸本)9798350301359

Deep learning provides more advanced solutions for Unmanned Aerial Vehicle (UAV) fault diagnosis, but most existing deep learning methods are designed for a single fault diagnosis task. A UAV is a complex system with multiple coupled components, requiring the simultaneous execution of several different Faulty Component Diagnosis (FCD) level 1 tasks and Fault Level Detection (FLD) level 2 tasks. In addition, scarcity of fault samples is a recognized problem. Therefore, we propose a Multi-Level Fault Diagnosis (MLFD) method for Fixed-Wing UAV (FW-UAV) based on multi-task learning. MLFD jointly trains the FCD and FLD tasks by Cross-stitch units under small samples, adaptively sharing the public information of the two tasks, enriching the fault diagnosis knowledge, and at the same time improving the model's ability to handle the two tasks. Finally, we validate the effectiveness of the proposed method in real FW-UAV failure data. © 2023 IEEE.

关键词： deep learning fault diagnosis multi-task learning shared information UAV

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Prediction of landslide displacement with dynamic features using intelligent approaches

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International Journal of Mining Science and Technology 2022年第3期32卷 539-549页

作者： Yonggang Zhang Jun Tang Yungming Cheng Lei Huang Fei Guo Xiangjie Yin Na Li Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical EngineeringTongji UniversityShanghai 200092China College of Civil Engineering Huaqiao UniversityXiamen 316000China School of Civil Engineering Qingdao University of TechnologyQingdao 266000China College of Civil Engineering and Architecture Wenzhou UniversityWenzhou 325000China Shenzhen Antai Data Monitoring Technology Co. Ltd.Shenzhen 518000China Key Laboratory of Disaster Prevention and Mitigation of Hubei Province China Three Gorges UniversityYichang 443002China School of Resources&Safety Engineering Central South UniversityChangsha 410083China

Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly *** the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic *** models may have bad performances,when the dynamic features of landslide triggers are *** paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic *** procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement *** case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise *** contrast,the accuracy can be significantly improved using the dynamic predictive *** paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.

关键词： Landslide displacement prediction Artificial intelligent methods Gated recurrent unit neural network CEEMDAN Landslide monitoring

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Robust Federated Semi-Supervised Learning for Medical Image Classification via Pseudo-Label Filtering

Robust Federated Semi-Supervised Learning for Medical Image ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Ziwei Guo, Shuyu Zhu, Mingzhu Bai, Tian Jilin University College of Software Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798350386226

Federated learning (FL) enables collaborative model training across multiple medical institutions to ensure data security. However, due to the variations in medical imaging equipment and regions at different medical institutions, FL methods usually suffer from insufficient data annotations and irrelevant noise within private datasets. To address these issues, a robust federated semi-supervised learning method via pseudo-label filtering (PFRFed) is introduced to utilize unlabeled data while mitigating the impact of noise data. Compared with existing federated semi-supervised learning methods, we propose a pseudo-label filtering mechanism with double dynamic thresholds, which allows the model to adopt more unlabeled data by adjusting the confidence and entropy thresholds at each stage of model training. Moreover, to reduce the degradation caused by noise data in private datasets from different clients, a noise-tolerant loss function and a grouping aggregation method based on the local model similarity are employed. The comparative experiments demonstrate the effectiveness of PFRFed, which has achieved the best classification accuracy of 95.20% and 88.72% on two public medical datasets. Also, PFRFed exhibits heightened resilience to variations in noisy data ratio and labeled data ratio, reaffirming its versatility and robustness. © 2024 IEEE.

关键词： Federated learning

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Graph Neural Networks with Learnable and Optimal Polynomial Bases

arXiv

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arXiv 2023年

作者： Guo, Yuhe Wei, Zhewei Gaoling School of Articial Intelligence Renmin University of China China Peng Cheng Laboratory China Beijing Key Laboratory of Big Data Management and Analysis Methods China MOE Key Lab of Data Engineering and Knowledge Engineering China

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the effectiveness of the model is highly dependent on the property of the polynomial basis. Consequently, two natural and fundamental questions arise: Can we learn a suitable polynomial basis from the training data? Can we determine the optimal polynomial basis for a given graph and node features? In this paper, we propose two spectral GNN models that provide positive answers to the questions posed above. First, inspired by Favard’s Theorem, we propose the FavardGNN model, which learns a polynomial basis from the space of all possible orthonormal bases. Second, we examine the supposedly unsolvable definition of optimal polynomial basis from Wang & Zhang (2022) and propose a simple model, OptBasisGNN, which computes the optimal basis for a given graph structure and graph signal. Extensive experiments are conducted to demonstrate the effectiveness of our proposed models. Our code is available at https://***/yuziGuo/FarOptBasis. © 2023, CC BY.

关键词： Graph neural networks

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Carbon-neutral pathway to mitigating transport-power grid cross-sector effects

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The Innovation 2024年第3期5卷 27-29页

作者： Jing Ma Huiwen Kong Jianxiao Wang Haiwang Zhong Bo Li Jie Song Daniel M.Kammen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic EngineeringNorth China Electric Power UniversityBeijing 102206China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China Peking University Ordos Research Institute of Energy Ordos 017000China State Key Laboratory of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China School of Electrical Engineering Guangxi UniversityNanning 530004China Department of Industrial Engineering and Management College of EngineeringPeking UniversityBeijing 100871China Energy and Resources Group and Goldman School of Public PolicyUniversity of CaliforniaBerkeleyBerkeleyCA 94720USA

Dear Editor,The growing demand for transportation energy has brought increasing challenges to reducing greenhouse gas ***,many countries or regions have proposed solutions to achieve carbon-neutral transportation such as the rapid expansion of the global electric vehicle(EV)***,these benefits are not *** the goal of decarbonization in electricity is achieved,the“pseudo net zero emissions”effect of the transportation sector will inevitably be accompanied by a quiet shifting of carbon responsibility.1 This shift is undoubtedly fatal for both net zero emissions at the national level and carbon responsibility at the industry level;however,the corresponding effects have not yet been clarified.

关键词： neutral doubt shifting

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A data-driven CEEMDAN-DRSCN-based RUL prediction method for UAV batteries 14

A data-driven CEEMDAN-DRSCN-based RUL prediction method for ...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Liao, Zihao Zhang, Ansi Li, Shaobo Leiyang Zhang, Yizong Zhou, Peng State Key Laboratory of Public Big Data Guizhou University Guiyang China School of Mechanical Engineering Guizhou University Guiyang China

ISBN: (纸本)9798350301359

Electric UAV propulsion systems have an advantage over fuel-powered UAVs. As the "heart" of an electric UAV, the remaining service life prediction of its electrical system is an important estimation parameter in battery health management. In this paper, we propose a data-driven incremental prediction method that integrates empirical modal decomposition and incremental learning models. First, a stochastic configuration network (SCN)-based incremental prediction model is designed to update the model input parameters by adding new data to overcome the in-flight data variation and employing Decay Regularization (DR) to avoid the output weight overfitting problem. Then, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is introduced to perform nonlinear mode decomposition on the UAV battery service data. Finally, experiments are conducted on the NASA UAV battery dataset. The experimental results show that the proposed CEEMDAN-DRSCN has good prediction performance and stability, prediction error is as low as 0.0004. © 2023 IEEE.

关键词： CEEMDAN Incremental learning Remaining useful life the Decay Regularization

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Generative Replay Method Based on Integrating Random Gating and Attention Mechanism 9

Generative Replay Method Based on Integrating Random Gating ...

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9th International Conference on Computer and Communications, ICCC 2023

作者： He, Yao Yang, Jing Li, Qinglang An, Hongchao Zhang, Fengyuan Guizhou University School of Mechanical Engineering Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (纸本)9798350317251

Artificial neural networks aspire to mimic human intelligence by constantly learning from a series of tasks without forgetting past knowledge. The most typical way to achieve this kind of learning is to store previously learned task data and replay previously stored data when training a new task. However, the performance of these methods will be affected with the reduction of memory during training. To solve this problem, a new generative replay algorithm is proposed in this paper - - A generative replay method based on the fusion of random gating and attention mechanism. This method is used to generate visual significance features for the model to remember past sample information. First, we introduce an attention mechanism into the generated model to address the issue of the quality of the generated replay data. This mechanism enhances the data salience of feature information during large-scale model training and allows the generated replay network to produce visual salience features, which essentially mimic the way a human observes. Secondly, because of the overfitting phenomenon in the training process of the model, a random gating mechanism is introduced to improve the generalization of the model and improve the accuracy of the model. Finally, we use the CIFAR100 and Permuted MNIST datasets to validate the efficacy of our method. © 2023 IEEE.

关键词： Neural networks

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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