检索结果-内蒙古大学图书馆

Prediction for Surface Subsidence of Shield Construction in Water-Rich Sand Egg Stratum Based on Edge Intelligence 1

引用

3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Gao, Yanxia Liu, Yiwen Mi, Chunqiao Tang, Pengju Shi, Yuanquan School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

The refinement and intelligent control of shield tunneling is the development trend of modern tunnel construction technology. In order to better predict and control the surface subsidence caused by shield excavation, this paper takes the shield construction of Luoyang Metro Line 2 from Longmen Station to Longmen Avenue Station as the background, and proposes a method based on edge intelligence for shield construction in water-rich sand egg strata. Methods for predicting land subsidence. First, low latency and faster data processing are achieved by collecting a large amount of data containing dynamic information about geological conditions and surrounding environments during the shield tunneling process;then using the iFogSim simulator to create different configurations;second, establishing support A surface subsidence model based on vector regression was established, and the model was deployed on the edge equipment;finally, the model was evaluated using the monitoring data of surface subsidence of the water-rich sand egg formation in Luoyang area. The research results show that the edge computing-based system has lower latency and higher processing speed than only deploying cloud data centers. After Pearson-related parameter tuning and model comparison training with Linear as the kernel function, the mean square error of the predicted value and the collected value of the surface subsidence is better than the other two kernel functions. The method proposed in this paper can provide real-time prediction service for large-scale surface subsidence prediction caused by shield construction, and is more practical. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Soils

来源：评论

学校读者我要写书评

暂无评论

A Miniaturized Microwave Limiter by Using T-Type LC Network Topology with GaAs Technology 13

A Miniaturized Microwave Limiter by Using T-Type LC Network ...

引用

13th Asia-Pacific International Symposium on Electromagnetic Compatibility and Technical Exhibition, APEMC 2022

作者： Wang, Jun Zhu, Haoran Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230039 China Anhui University Ministry of Education Key Lab of Intelligent Computing & Signal Processing Hefei230039 China

ISBN: (数字)9781665416719

ISBN: (纸本)9781665416719

A miniaturized ultra-wideband microwave limiter with low insertion loss is presented in this paper. This limiter adopts a three-stage antiparallel diode structure. A T-type LC network topology consisting of two spiral inductors and a parallel grounded metal-insulator-metal (MIM) capacitor is utilized to replace the traditional λ/4 transmission line in series between the limiting circuits. Compared with the traditional topology, the proposed limiter has the advantages of miniaturization and ultra-wideband. The area of the presented microwave limiter chip is only 1.25×0.75mm2. The simulation results show that the limiter can work in 0.05-18GHz. Insertion loss is less than 0.65dB at 18GHz. The output power can be maintained below 15dBm with a maximum power input of 40dBm continuous wave. © 2022 IEEE.

关键词： Network topology

来源：评论

学校读者我要写书评

暂无评论

Meta-feature-based Concept Evolution Detection on Feature Streams

Meta-feature-based Concept Evolution Detection on Feature St...

引用

6th International Conference on intelligent Computing and signal processing (ICSP)

作者： Yufeng Guo Peng Zhou Yanping Zhang Xin Jiang School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

Data in the real world is often not static but generated and processed in streams, such as real-time adjustment of device setting parameters and real-time GPS positioning data. Feature streams means the number of samples is fixed, and their features are generated and arrive individually over time. A significant challenge of learning from online streaming data is a phenomenon known as concept evolution, that the concept of the data may change over time. In the streaming feature scenario, we define meta-features as univariate statistics describing data distribution and use meta-features to capture the data distribution and statistical properties of concepts. Therefore, an efficient Meta-Feature-based Concept Evolution Detection framework on Feature Streams (MF-CED-FS) is proposed, which consists of a sliding window, meta-feature vector similarity discrimination, and a concept detection method based on a weighted bipartite graph. Extensive experiments on real-world high-dimensional datasets verify the effectiveness of MF-CED-FS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis

arXiv

引用

arXiv 2024年

作者： Yang, Shangshang Chen, Mingyang Wang, Ziwen Yu, Xiaoshan Zhang, Panpan Ma, Haiping Zhang, Xingyi Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei230601 China Anhui Province Key Laboratory of Intelligent Computing and Applications China Department of Information Materials Intelligent Sensing Laboratory of Anhui Province China

Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poorly, failing to provide high robustness against noise in students’ interactions. Besides, lower-order exercise latent representations obtained in shallow layers are not well explored when learning the student representation. To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency graphs, respectively. Specifically, the latter two graphs are first disentangled from the interaction graph. Then, the student representation is learned from the interaction graph by a devised meta multigraph learning module;multiple learnable propagation paths in this module enable current student latent representation to access lower-order exercise latent representations, which can lead to more effective nad robust student representations learned;the exercise and concept representations are learned on the relation and dependency graphs by graph attention modules. Finally, a novel diagnostic function is devised to handle three disentangled representations for prediction. Experiments show better performance and robustness of DisenGCD than state-of-the-art CD methods and demonstrate the effectiveness of the disentangled learning framework and meta multigraph module. The source code is available at https://***/BIMK/intelligent-Education/tree/main/DisenGCD. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Scientific Reading Comprehension with Sentences Selection and Ranking 12th

Scientific Reading Comprehension with Sentences Selection a...

引用

12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Chen, Jialei Wang, Weihua Shao, Shuai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9783031446986

Scientific Machine Reading Comprehension (SMRC) aims to understand scientific long text by providing answers for the given questions. Most existing methods trend to answer the question using Transformer-based models. However, in the scientific domain, the original text is longer than the general domain. In this paper, we proposed a model that consists of a content retrieval module and a pre-trained model module. The content retrieval module finds the most semantically relevant sentences from the text and re-rank them. The seleted sentences and question will be input into the pre-trained model to get the answers. This model could overcome the length limitation of Transformer model length while achieving impressive results. Our model achieved 0.45 score of RougeL, resulting in the second place in the NLPCC2023 Shared Task2. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

Attention Mechanism Based Technical Phrase Extraction for Patent Text

Attention Mechanism Based Technical Phrase Extraction for Pa...

引用

6th International Conference on intelligent Computing and signal processing (ICSP)

作者： Xin Jiang Peng Zhou Shu Zhao Yufeng Guo School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

Technological innovation is becoming one of the critical factors in promoting social development all over the world. The vigorous development of patent applications in recent years provides an opportunity to reveal the inherent laws of innovation, but it also puts forward higher requirements for patent mining technology. An essential step in patent text mining is to build a technical portrait for each patent, that is, to identify the technical phrases involved, which can summarize and represent the patent from a technical perspective. Previous technical phrase extraction methods thoroughly used technical phrases' characteristics and the relationship between technical phrases. Regarding our observations, the relationship between patent texts and technical phrases is also essential. Specifically, critical technical phrases are more relevant to the patent text and can be discovered by the attention mechanism. Motivated by this, we propose an unsupervised technical phrase extraction method based on the attention mechanism named UTESC. Self-attention captures the importance of technical phrases in sentences, and cross-attention captures the relevance between technical phrases and patents. Extensive experiments and algorithm comparisons on patent datasets have proven the effectiveness of our algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Geological Disaster Named Entity Recognition with Small Samples Based on Data Augmentation and Prompt Engineering 24

Geological Disaster Named Entity Recognition with Small Samp...

引用

2024 7th International Conference on computer Information Science and Artificial Intelligence, CISAI 2024

作者： Ma, Xiangfei Li, Lin School of Computer Science Qinghai Normal University Qinghai Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining China Academy of Plateau Science and Sustainability Qinghai Normal University Qinghai Xining China

ISBN: (纸本)9798400707254

This paper uses a large language model to perform generative data enhancement on the original small sample data by performing random synonym replacement and random mask filling operations. In accordance with the reasoning logic of the large language model, three prompt templates are designed and the reasons are explored. Experiments show that when the parameters remain unchanged, the data enhanced by this method has been greatly improved under the three prompt templates, alleviating the difficulty of low resources of geological disaster data. And by comparing the performance of different instructions under different learning rates, the fine-tuning learning rate range suitable for the field of geological disasters is summarized. The limitation is that it is constrained by local computing resources, which reduces the parameter scale of LLM, and the recognition performance is low for extremely long or complex texts. © 2024 Copyright held by the owner/author(s).

关键词： Engineering geology

来源：评论

学校读者我要写书评

暂无评论

Model-Based Offline Policy Optimization with Adversarial Network 26

Model-Based Offline Policy Optimization with Adversarial Net...

引用

26th European Conference on Artificial Intelligence, ECAI 2023

作者： Yang, Junming Chen, Xingguo Wang, Shengyuan Zhang, Bolei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China School of Modern Posts Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781643684369

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model's uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately. © 2023 The Authors.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Robust Framework Based on Mutual Information and Contrastive Learning for Multimodal Violence Detection in Videos 23

A Robust Framework Based on Mutual Information and Contrasti...

引用

15th International Conference on Machine Learning and Computing, ICMLC 2023

作者： Xiong, Guoding Zhou, Yongmei Zhou, Dong Lin, Nankai School of Information Science and Technology Guangzhou Key Laboratory of Multilingual Intelligent Processing Guangdong University of Foreign Studies Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781450398411

With the development of modern information, the security of online videos content is receiving more and more *** work often ignores the correlation between different modalities. In order to solve the above problem, the task of violence detection is proposed to identify and locate violent frames in videos. In this paper, we propose a robust framework based on mutual information and contrastive learning for multimodal violence detection in videos. We use mutual information maximization to improve the correlation between audio and visual modalities. Meanwhile, we leverage contrastive learning to control modality fusion to capture modality invariance. We propose a robust framework consisting of three modules: 1) utilize a graph convolutional neural network module to capture global relationships across multiple video frames;2) demonstrate a mutual information module to strengthen the correlation between audio and visual modalities;3) utilize the contrastive learning module to capture the modality invariance to facilitate modality fusion. For comparison with previous studies, we conduct extensive experiments on a public XD-Violence dataset. Experimental results show that our method achieves state-of-the-art performance. © 2023 ACM.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

SCENERY:a lightweight block cipher based on Feistel structure

引用

Frontiers of computer Science 2022年第3期16卷 193-202页

作者： Jingya FENG Lang LI Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China

In this paper,we propose a new lightweight block cipher called *** main purpose of SCENERY design applies to hardware and software *** is a 64-bit block cipher supporting 80-bit keys,and its data processing consists of 28 *** round function of SCENERY consists of 84×4 S-boxes in parallel and a 32× 32 binary matrix,and we can implement SCENERY with some basic logic *** hardware implementation of SCENERY only requires 1438 GE based on 0.18 um CMOS technology,and the software implementation of encrypting or decrypting a block takes approximately 1516 clock cycles 0118-bit microcontrollers and 364 clock cycles on 64-bit *** with other encryption algorithms,the performance of SCENERY is well balanced for both hardware and *** the security analyses,SCENERY can achieve enough security margin against known attacks,such as differential cryptanalysis,linear cryptanalysis,impossible differential cryptanalysis and related-key attacks.

关键词： lightweight block cipher feistel structure cryptanalysis internet of things

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：