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第35届中国控制与决策会议

作者： Penghua Li Kangheng Shan Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications

Many existing methods of forecasting the stateof-health(SOH) assume that training and testing data follow the same *** model based on dataset under one working condition may be ineffective for the dataset under another working condition due to the distribution *** order to meet this challenge,this paper proposes an improved method Mutual Information Domain-Adversial Neural Networks(DANN) based on domain adaptation,which improves the domain discriminator to better extract domain invariant *** addition,to avoid the loss of target information,the mutual information among target features,source features,and original target data is calculated to fix the features on the target site during the migration *** from the traditional methods,we only use 40% of the data sets for training,and the rest are used for prediction,so we can complete the prediction of more *** results show that this method can accurately and stably predict SOH.

关键词： transfer learning state-of-health lithium-ion battery neural network

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Course Homework Reform in Universities Based on Extended Reality 17th

Course Homework Reform in Universities Based on Extended Rea...

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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Fu, Jinrong Liu, Yiwen Mi, Chunqiao Peng, Xiaoning Xiao, Jianhua School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924486

In view of the serious issues commonly existing with coursework in Chinese universities at present, such as its original function weakening or being suppressed, its form being too abstract and lack of elaborate design. In order to resolve these problems observed in usual homework, this paper proposes a reform scheme for cloud coursework based on Extended Reality and Bloom model. It mainly includes five key parts, such as interaction, scene, comprehensive evaluation, digital twin and data analysis. In addition, the reform’s effectiveness is illustrated through amassing and comparing the feedback of college students for teaching. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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LAGIM:A Label-Aware Graph Interaction Model for Joint Multiple Intent Detection and Slot Filling

LAGIM:A Label-Aware Graph Interaction Model for Joint Multip...

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第35届中国控制与决策会议

作者： Penghua Li Ziheng Huang Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications

Multi-intent spoken language understanding joint model can handle multiple intents in an utterance and is closer to complicated real-world scenarios,attracting increasing ***,existing research(1) usually focuses on identifying implicit correlations between utterances and one-hot encoding while ignoring intuitive and explicit original label characteristics;(2) only considers the token-level intent-slot interaction,which results in the limitation of the *** this paper,we propose a Label-Aware Graph Interaction Model(LAGIM),which captures the correlation between utterances and explicit labels' semantics to deliver enriched ***,a global graph interaction module is constructed to model the sentence-level interaction between intents and ***,we propose a novel framework to model the global interactive graph based on the injection of the original label semantics,which can fuse explicit original label features and provide global *** results show that our model outperforms existing approaches,achieving a relative improvement of 11.9% and 2.1%overall accuracy over the previous state-of-the-art model on the MixATIS and MixSnips datasets,respectively.

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sSecure Net: A Hybrid CNN-LSTM-based Intrusion Detection System for Securing IoT Networks 25

sSecure Net: A Hybrid CNN-LSTM-based Intrusion Detection Sys...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control Engineering

作者： Ovi Abul Hussain Zigang Chen Haihua Zhu Chongqing Key Laboratory of Cyberspace Security Monitoring and Governance & Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798400712647

The widespread deployment of IoT devices has seen the emergence of a huge security threat, and IoT networks are the favorite targets for numerous cyberattacks. Due to the scarce resources in most IoT devices, the conventional intrusion detection systems (IDS) are unable to catch up with the enormous traffic in IoT networks. This paper suggests a new intrusion detection model based on CNN-LSTM, incorporating deep learning and statistical filtering to increase the intrusion detection in IoT networks. The model uses Convolutional Neural Networks (CNN) to capture the spatial relationships and Long Short-Term Memory (LSTM) networks to capture the temporal relationships in IoT network traffic. The model also uses statistical filtering techniques, including Median Filtering and Standard Deviation-based Filtering, to pre-process the traffic and eliminate the noise and the outliers, making the model's precision improved. The model is tested based on the Edge-IIoTset dataset, comprised of different diverse attack types, including Denial of Service (DoS), SQL injection, Ransomware, and Man-in-the-Middle (MITM). The model gives 94.99% precision, reflecting the model's precision in detecting different intrusions. This paper sets the efficacy in the use of the integration between the use of CNN-LSTM and statistical filtering to build an efficient, scalable, and precise intrusion detection model in IoT networks. The findings offer a real-time solution to increase IoT security, in particular, intrusion detection in IoT networks in real-time.

关键词： Anomaly Detection

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CQUPT-FL: Cross-Domain Sharing and Security Awareness and Early Warning Platform of Health Science big data

CQUPT-FL: Cross-Domain Sharing and Security Awareness and Ea...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yunpeng Xiao Qunqing Zhang Wanjing Zhao Xufeng Li Jinhua Peng Haonan Mo Haipeng Zhu Fei Tang Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

Federated learning (FL) is a rapidly growing research area in machine learning, but it is problematic. It has been questioned whether or not existing FL libraries are practical in the area of medical privacy. To address these issues, we developed the CQUPT-FL system. The system focuses on resolving the conflict between data integrity and medical data privacy protection in cross-domain and cross-institution collaborative analysis. CQUPT-FL supports distributed computing and stand-alone simulation computing methods. To deal with the problems of heterogeneity, data domain diversity, and effective data scarcity, we adopted key technologies such as multi-party secure computing and holistic information representation and studied user identification, privacy protection, and heterogeneous user alignment to achieve sustainable Cross-domain and cross-platform data fusion of letters. The goal of introducing the CQUPT-FL system is to improve the level of data privacy protection and enhance the data privacy protection mechanism, solve the machine learning dilemma in the field of medical privacy, and provide a reliable solution for cross-domain collaborative analysis.

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Few-Shot Object Detection Based On Label Constrained data Augmentation

Few-Shot Object Detection Based On Label Constrained Data Au...

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International Conference on big data and Information Analytics (bigDIA)

作者： Yan Shi Guoyin Wang Qun Liu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

The majority of object detection methods typically depend on a significant quantity of annotated data, while few-shot object detection (FSOD) endeavors to identify novel classes of objects using a limited number of training instances. However, the limited number of samples leads to the problem of disparate data distributions between the source and target domains, which makes the generalization ability of the detector usually weak. In this paper, we combine data augmentation with fine-tuning to design a pseudo-labeled constrained model called LCDA, aiming to obtain high-quality pseudo-labels to effectively enrich the training examples. Furthermore, we leverage the pre-trained CLIP model to enhance the quality of pseudo-labels by restricting the category information as well as the designed bounding box consistency criterion. The experimental outcomes demonstrate that our model outperforms the existing models on two public datasets across various shot scenarios. The average enhancement of our method on different shots is 1.2AP, 1.6AP, 2.3AP, 2.7AP, and 4.2AP. We also validate the performance of the model in real applications of the USV dataset which shows an improvement of 1.9AP over the baseline methods. All demonstrate the effectiveness of our model.

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Cross-Ray Neural Radiance Fields for Novel-view Synthesis from Unconstrained Image Collections

Cross-Ray Neural Radiance Fields for Novel-view Synthesis fr...

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International Conference on Computer Vision (ICCV)

作者： Yifan Yang Shuhai Zhang Zixiong Huang Yubing Zhang Mingkui Tan South China University of Technology Pazhou Lab Guangzhou Shiyuan Electronics Co. Ltd Key Laboratory of Big Data and Intelligent Robot Ministry of Education

Neural Radiance Fields (NeRF) is a revolutionary approach for rendering scenes by sampling a single ray per pixel and it has demonstrated impressive capabilities in novel-view synthesis from static scene images. However, in practice, we usually need to recover NeRF from unconstrained image collections, which poses two challenges: 1) the images often have dynamic changes in appearance because of different capturing time and camera settings; 2) the images may contain transient objects such as humans and cars, leading to occlusion and ghosting artifacts. Conventional approaches seek to address these challenges by locally utilizing a single ray to synthesize a color of a pixel. In contrast, humans typically perceive appearance and objects by globally utilizing information across multiple pixels. To mimic the perception process of humans, in this paper, we propose Cross-Ray NeRF (CR-NeRF) that leverages interactive information across multiple rays to synthesize occlusion-free novel views with the same appearances as the images. Specifically, to model varying appearances, we first propose to represent multiple rays with a novel cross-ray feature and then recover the appearance by fusing global statistics, i.e., feature covariance of the rays and the image appearance. Moreover, to avoid occlusion introduced by transient objects, we propose a transient objects handler and introduce a grid sampling strategy for masking out the transient objects. We theoretically find that leveraging correlation across multiple rays promotes capturing more global information. Moreover, extensive experimental results on large real-world datasets verify the effectiveness of CR-NeRF. The code and data can be found at https://***/YifYang993/***.

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Multi-Granular Differential Evolution Algorithm for Multi-Agent Task Allocation

Multi-Granular Differential Evolution Algorithm for Multi-Ag...

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International Conference on big data and Information Analytics (bigDIA)

作者： Peng Gao Qun Liu Haihuan Jiang Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

The multi-agent task allocation presents a fundamental challenge in the field of multi-agent systems, especially in uncertain environments. Although extensive research has been conducted on the multi-agent task allocation in deterministic environments, discussions around the multi-agent task allocation in uncertain environments are relatively scarce. In reality, uncertain data is more common in practical decision-making processes. To address the multi-agent task allocation problem in uncertain environments, this study frames it as a noisy optimization problem and proposes a novel Multi-Granular Differential Evolution (MGDE) algorithm to solve it. MGDE combines the powerful differential evolution (DE) with the granular-ball computing which has high robustness in noise. The proposed MGDE is compared with other three state-of-the-art algorithms on 12 scenarios encompassing 6 agent and task quantity combinations and 2 uncertainty levels. Experimental results demonstrate the superior performance of MGDE.

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A Multi-Agent Communication Based Model for Nested Named Entity Recognition

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2021年 29卷 2123-2136页

作者： Li, Canguang Wang, Guohua Cao, Jin Cai, Yi South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot SCUT Guangzhou 510006 Peoples R China

Named entity recognition (NER) is a basic task in natural language processing. Traditionally, sequence labeling methods are applied to named entity recognition and achieve good performance. However, sequence labeling methods can not be straightly applied to recognize nested named entities where an entity is included in another entity. Recently, some new methods are proposed for nested named entity recognition. Most of them ignore that entity type information can help recognize entity boundaries or ignore that entity boundary information can help recognize entity type, which limits the performance of nested NER. Considering the effect of entity type information and entity boundary information, in this paper, we propose a multi-agent communication module to utilize these two kinds of information. Our multi-agent communication module contains a type labeling agent and a boundary labeling agent. The type labeling agent can utilize boundary information from boundary labeling agent to recognize entity type. And the boundary labeling agent can utilize type information from type labeling agent to recognize entity boundaries. They communicate and collaborate iteratively to finish the entity boundary recognition. Compared with previous methods, with the assist of entity type information and entity boundary information, the performance of boundary recognition improves. The improvement of boundary recognition is beneficial to recognizing nested named entities, which improves the performance of nested named entity recognition. Empirical experiments are conducted on three nested NER datasets. And the experimental results show the effectiveness of our model.

关键词： Labeling Hidden Markov models Task analysis Speech processing Feature extraction data mining DNA Nested named entity recognition multi-agent communication information extraction

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Teaching Reform and Practice for the Course of Web Application Development Technology Under the Background of Online Education 17th

Teaching Reform and Practice for the Course of Web Applicati...

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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Dong, Yi Xiaoning, Peng Xian, Zhang Chunqiao, Mi Yiwen, Liu Taiguo, Qu School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924455

With the advent of knowledge economy, online education relies on the Internet and mobile terminals, it breaks the limitation of time and space of traditional education. Online education has gradually becoming a new way for people to learn. Under the special background of COVID-19 since 2020, online education has been well applied. The course of Web Application Development Technology is not only a course which is the core course in software development for computer majors, but also is a course with strong practicality and application. This paper analyzes the problems existing in the online teaching of the courses of web course under the background of online education, and presents a new scheme of the teaching reform measures for the course according to the characteristics of online education and the course of Web Application Development Technology which is well applied by the author’s own practices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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