检索结果-内蒙古大学图书馆

2022 International Conference on Power Energy Systems and Applications, ICoPESA 2022

作者： Ji, Ping Zhu, Quan Key Lab. of Unmanned Aer. Vehicle Development Data Application of Anhui Higher Education Institutes Ma'anshan China Maanshan Wireless Sensor Network and Intelligent Perception Engineering Technology Research Center Wanjiang China University of Technology Maanshan China Maanshan University Ma'anshan China

ISBN: (纸本)9781665410977

With the rapid development of clean energy generation technology, a large number of distributed power sources are integrated into the local power grid, resulting in the intensification of disturbance signals in the power grid. It is particularly important to effectively classify and identify the disturbance signals in the power grid. This paper presents a recognition method based on energy difference and support vector machine. The method extracts signal feature vectors by calculating the energy difference of power grid signals and sends them to support vector machine for classification. Experiments were carried out on the power grid signals with and without noise. Experiments show that this method can effectively classify and recognize the disturbance signals, and the recognition rate is greatly improved. The effectiveness of the method is proved. This paper provides a theoretical basis for the improvement of power grid quality and intelligent monitoring management in the future. © 2022 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection

SAFL-Net: Semantic-Agnostic Feature Learning Network with Au...

引用

International Conference on Computer Vision (ICCV)

作者： Zhihao Sun Haoran Jiang Danding Wang Xirong Li Juan Cao Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Mathematics Science University of Chinese Academy of Sciences MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China

Since image editing methods in real world scenarios cannot be exhausted, generalization is a core challenge for image manipulation detection, which could be severely weakened by semantically related features. In this paper we propose SAFL-Net, which constrains a feature extractor to learn semantic-agnostic features by designing specific modules with corresponding auxiliary tasks. Applying constraints directly to the features extracted by the encoder helps it learn semantic-agnostic manipulation trace features, which prevents the biases related to semantic information within the limited training data and improves generalization capabilities. The consistency of auxiliary boundary prediction task and original region prediction task is guaranteed by a feature transformation structure. Experiments on various public datasets and comparisons in multiple dimensions demonstrate that SAFL-Net is effective for image manipulation detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Modeling the impacts of land use/cover change on sediment load in wetlands of the poyang lake basin

Modeling the impacts of land use/cover change on sediment lo...

引用

International Conference on Hydrology and Management of Forested Wetlands

作者： Chen, X. Bao, S. Li, H. Wu, Z. Fu, W. Cai, X. Zhao, H. Liu, Y. Peng, G. State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 China Key Lab. of Poyang Lake Ecological Environment Resource Development Jiangxi Normal University Nanchang Jiangxi China China Data Center University of Michigan United States Environmental Monitoring Centre of Jiangxi Province Nanchang Jiangxi 330029 China

ISBN: (纸本)1892769530

Poyang Lake watershed mainly consists of Poyang Lake and its five sub-watersheds. Its wetlands, the largest bird habitat in Asia, play an important role in biodiversity and ecologic protection. With the rapid economic growth and urbanization, the wetlands in Poyang Lake area have been shrinking and face many environmental challenges. To better understand the environmental and ecologic changes of wetlands in the Poyang Lake watersheds, we applied RS/GIS techniques and a mathematical model in a study of the relationship between land use/cover changes and sediment load of the Poyang Lake watershed. RS classification was used to detect the land use/cover change, and a relational searching algorithm of spatial objects was proposed to improve the classification accuracy of wetlands. The driving forces for the land use/cover change were also analyzed. Soil erosion by water, as a big environmental problem, reflects the process of surface water related to non - point source pollution that influences water quality, the environment, and ecology of wetlands. In this study GIS and RS technologies are employed to predict annual soil loss. This work prioritizes erosion-prone areas which are important sources of sediment and highlights the severity of soil erosion in the watershed. SSC (suspended sediment concentration) data were collected for the study period. The relationships among land use/cover change, erosion area variations and SSC alterations are examined for the study period. The results show that the land use/cover change in the watershed lessen sediment sources and led to less SSC load to the wetland. The Poyang Lake is less impacted by the SSC in 2000 compared with 1991.

关键词： Wetlands

来源：评论

学校读者我要写书评

暂无评论

A Multi-Step Speed Prediction Method for Traffic Networks Based on MAF-GCN-Seq2Seq Model 2

A Multi-Step Speed Prediction Method for Traffic Networks Ba...

引用

2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Long, Yi Xiao, Hongbo Xiao, Jianhua Peng, Xiaoning School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Hunan Huaihua418000 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9781665464017

Accurately predicting the future trend of traffic flow is of great significance to the planning of urban traffic. The great challenge of predicting traffic flow stems from the complexity and uncertainty of traffic flow. To address the complexity of traffic flow, this paper proposes a MAF-GCN 2 Seq model. The model uses a moving average filter to filter the time series data, extracts the spatial features between each node through a graph convolutional neural network, and forms a time series matrix for input into the encoder (GRU), and introduces a self-attention mechanism to mine the spatial features. time-domain features for multi-step traffic speed prediction. In order to verify the advanced nature of the model, this paper uses the traffic flow data set in Shenzhen to predict the traffic flow. Through the comparison experiments, it can be seen that the prediction accuracy of the MAF-GCN 2 Seq model reaches 90%, which has obvious advantages compared with other models. © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A study on the differences of attitude learning and learning behavior sequences for university computer MOOC+ SPOC Course 15

A study on the differences of attitude learning and learning...

引用

15th International Conference on Computer Science and Education, ICCSE 2020

作者： Changhua, Zhao Chunqiao, Mi Huaihua University School of Computer Science and Engineering Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua Hunan418000 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China

ISBN: (纸本)9781728172675

The outbreak of COVID-19 make University Computer MOOC+SPOC course face opportunities and challenges. There are some problems in MOOC+SPOC course teaching, such as learners' curiosity decreasing, lack of accomplishment and etc. In order to deal with these problems, this study explores the correlation and differences in the learning behavior sequences of learners with different attitudes to high happiness, medium happiness, and low happiness. Through the learners' comments on University Computer MOOC+SPOC course, the learner's attitude information is tapped. By analyzing learning behavior sequences, this study finds out the degree of learning behavior change of learners with different attitudes. The result of this study can improve the existing University Computer MOOC+SPOC instructional design and promote the learning mode to move towards intelligent personalized adaptive learning. © 2020 IEEE.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Predicting user influence based on improved linear threshold model in social networks

引用

Journal of Computational Information Systems 2014年第14期10卷 6151-6160页

作者： Tian, Zhongsheng Wang, Ying Zhao, Qiuyue Zuo, Wanli College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Lab. of Symbolic Computation and Knowledge Engineering Attached to the Ministry of Education Changchun China

With the scale of social networks growing rapidly, the amount of user participating in it increases at astonishing speed. Predicting user influence in social networks is an interesting and useful research direction. There are lots of works on this area, mainly covering the following aspects: Maximize Influence, Influence Diffusion, Predicting Influential Users and Predicting social influence. The common issue of above aspects is that they can't calculate the precise user influence. In this work, we propose a novel method to give each user influence a precise value in social networks. The proposed method is based on Improved Linear Threshold Model (ILTM). To build ILTM, we take a series of measures to overcome the deficiencies brought by Linear Threshold Model, such as assigning random threshold to node, cold start. So we adopt user similarity instead of random threshold and utilize cluster algorithm and link analysis algorithm to surmount cold start. © 2014 by Binary Information Press

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

arXiv

引用

arXiv 2023年

作者： Xu, Fangzhi Lin, Qika Han, Jiawei Zhao, Tianzhe Liu, Jun Cambria, Erik The School of Computer Science and Technology Xi’an Jiao-tong University Shaanxi Xi’an710049 China The Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering National Engineering Lab for Big Data Analytics Shaanxi Xi’an710049 China The School of Computer Science and Engineering Nanyang Technological University Singapore

Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs) have emerged as a noteworthy innovation in natural language processing (NLP). However, the question of whether LLMs can effectively address the task of logical reasoning, which requires gradual cognitive inference similar to human intelligence, remains unanswered. To this end, we aim to bridge this gap and provide comprehensive evaluations in this paper. Firstly, to offer systematic evaluations, we select fifteen typical logical reasoning datasets and organize them into deductive, inductive, abductive and mixed-form reasoning settings. Considering the comprehensiveness of evaluations, we include 3 early-era representative LLMs and 4 trending LLMs. Secondly, different from previous evaluations relying only on simple metrics (e.g., accuracy), we propose fine-level evaluations in objective and subjective manners, covering both answers and explanations, including answer correctness, explain correctness, explain completeness and explain redundancy. Additionally, to uncover the logical flaws of LLMs, problematic cases will be attributed to five error types from two dimensions, i.e., evidence selection process and reasoning process. Thirdly, to avoid the influences of knowledge bias and concentrate purely on benchmarking the logical reasoning capability of LLMs, we propose a new dataset with neutral content. Based on the in-depth evaluations, this paper finally forms a general evaluation scheme of logical reasoning capability from six dimensions (i.e., Correct, Rigorous, Self-aware, Active, Oriented and No hallucination). It reflects the pros and cons of LLMs and gives guiding directions for future works. © 2023, CC BY.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Image Contrast Enhancement Algorithm Based on Retinex

An Adaptive Image Contrast Enhancement Algorithm Based on Re...

引用

Chinese Automation Congress (CAC)

作者： Guifang Shao Fengqiang Gao Tiejun Li Rong Zhu Ting Pan Yuwen Chen Xiamen Key Lab. of Big Data Intelligent Analysis and Decision Xiamen University Xiamen China School of Information Engineering Jimei University Xiamen China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Image contrast enhancement is an essential operation for various image processing applications, especially for segmentation and feature extraction. However, different contrast enhancement methods contain various performance on the same images. To automatically choose the suitable algorithm for image contrast enhancement, in this paper, we put forward a different Retinex based method according to image feature fusion. First, five features, including gray value, contrast, definition, edge and entropy, are defined to characterize various image. Then, five fusion rules are designed to distinguish images with low contrast, foggy, monotonous color, rich in detail or color. Finally, the single scale Retinex (SSR), the multi-scale Retinex (MSR) and MSR in HSV color space (MSR_HSV) are introduced to improve image contrast. Experiments on various images drawn from different data sets prove the effectiveness of our proposed method.

关键词： Image color analysis Feature extraction Image edge detection Image quality Gray-scale Filtering algorithms Color

来源：评论

学校读者我要写书评

暂无评论

Joint Participation Incentive and Network Pricing Design for Federated Learning 42

Joint Participation Incentive and Network Pricing Design for...

引用

42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Ding, Ningning Gao, Lin Huang, Jianwei Northwestern University Department of Electrical and Computer Engineering EvanstonIL60208 United States Shenzhen Research Institute of Big Data Shenzhen518172 China Harbin Institute of Technology Sch. of Electronics and Info. Eng. and the Guangdong Prov. Key Lab. of Aerosp. Commun. and Networking Technol. Shenzhen China The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China

ISBN: (纸本)9798350334142

Federated learning protects users' data privacy though sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users' contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users' private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server's cost by up to 24.87% comparing with the state-of-the-art benchmarks. © 2023 IEEE.

关键词： Costs

来源：评论

学校读者我要写书评

暂无评论

Integrity constraint validation for ontology using modularization

引用

Journal of Information and Computational Science 2014年第8期11卷 2705-2713页

作者： Cui, Xianji Ouyang, Dantong Ye, Yuxin Su, Jing College of Computer Science and Technology Jilin University Changchun 130012 China Key Lab. of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun 130012 China National Engineering Research Center of Geophysics Exploration Instruments Jilin University Changchun 130026 China

In the last years, integrity constraint validation over the ontology with expressive description logics is a prominent reasoning service in ontology engineering, as integrity constraints are added into the ontology to guarantee the legal states of ontology data. The integrity constraint validation can be converted into the conjunctive query answering, we thus check the satisfaction of integrity constraints in ontology knowledge base based on the tableau algorithm and further use the existing reasoning systems. With the increase of the ontology data set, however, these reasoning systems reveal the bad performance in real-life ontologies. In order to reduce the amount of information that must be taken into account, we apply the idea of modularization to break down the terminologies and assertions with respect to integrity constraints, and get smaller chunks for them. The empirical results are presented to demonstrate the high efficiency and an improvement of integrity constraint validation. Copyright © 2014 Binary Information Press.

关键词： Ontology

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：