检索结果-内蒙古大学图书馆

Efficient zonal diagnosis with maximum satisfiability

Science China(Information Sciences) 2018年第11期61卷 17-30页

作者： Meng LIU Dantong OUYANG Shaowei CAI Liming ZHANG College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of EducationJilin University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences

Model-based diagnosis(MBD) has been widely acknowledged as an effective diagnosis ***, for large scale circuits, it is difficult to find all cardinality-minimal diagnoses within a reasonable time. This paper proposes a novel method that takes a significant step in this direction. The idea is to divide a circuit into zones and compute the cardinality-minimal diagnoses by finding subset-minimal diagnoses with cardinality-minimal via a maximum satisfiability(MaxSAT) solver on an abstracted circuit that is composed of these zones instead of all components. We also propose a new propagate-extend method for extending the seed-TLDs to obtain all cardinality-minimal diagnoses efficiently. We implement our method with a state-of-the-art core-guided MaxSAT solver, and present evidence that it significantly improves the diagnosis efficiency on ISCAS-85 circuits. Our method outperforms SATbD, which was recently shown to outperform most complete MBD approaches using satisfiability(SAT).

关键词： model-based diagnosis MaxS AT subset-minimal diagnoses cardinality-minimal diagnoses zone propagate-extend

来源：评论

学校读者我要写书评

暂无评论

Improved fluid search optimization-based real-time weed mapping

引用

Information Processing in Agriculture 2020年第3期7卷 403-417页

作者： Chengcheng Chen Shengsheng Wang Xianchang Wang Helong Yu Ruyi Dong College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun 130012China College of Information Technology Jilin Agricultural UniversityChangchun 130118China Chengdu Kestrel Artificial Intelligence Institute Chengdu 611730China

In the field of agriculture,variable-rate herbicide spraying(VRHS)technology has been used to solve the low efficiency of pesticides and crop chemical *** key of VRHS is the quick and precise identification of weeds from field images,which forms a weed *** search optimization(FSO)was able to simplify the threshold optimization process to create a weed map,which simulated the fluid flowing from high pressure to low pressure,but it is time consuming and often converges ***,an explosion mechanism and a twophase optimization were introduced to improve the FSO-based segmentation *** of segmentation weeds from a corn field at seedling growth stage showed that the IFSO algorithm obtained the best accuracy of 93.3%and the least running time of 0.019 s,compared with the standard PSO,GA,and FSO algorithms.

关键词： Variable-rate herbicide spraying Weed map Fluid search optimization algorithm Otsu

来源：评论

学校读者我要写书评

暂无评论

Independently keypoint Learning for Small Object Semantic Correspondence

SSRN

引用

SSRN 2024年

作者： Jin, Hailong Li, Huiying Jilin University College of Computer Science and Technology Jilin Province Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun130012 China

Semantic correspondence remains a challenging task for establishing correspondences between a pair of images with the same category or similar scenes due to the large intra-class appearance. In this paper, we introduce a novel problem called 'Small Object Semantic Correspondence (SOSC).' This problem is challenging due to the close proximity of keypoints associated with small objects, which results in the fusion of these respective features. It is difficult to identify the corresponding key points of the fused features, and it is also difficult to be recognized. To address this challenge, we propose the keypoint Bounding box-centered Cropping (KBC) method, which aims to increase the spatial separation between keypoints of small objects, thereby facilitating independent learning of these keypoints. The KBC method is seamlessly integrated into our proposed inference pipeline and can be easily incorporated into other methodologies, resulting in significant performance enhancements. Additionally, we introduce a novel framework, named KBCNet, which serves as our baseline model. KBCNet comprises a Cross-Scale Feature Alignment (CSFA) module and an efficient 4D convolutional decoder. The CSFA module is designed to align multi-scale features, enriching keypoint representations by integrating fine-grained features and deep semantic features. Meanwhile, the 4D convolutional decoder, based on efficient 4D convolution, ensures efficiency and rapid convergence. To empirically validate the effectiveness of our proposed methodology, extensive experiments are conducted on three widely used benchmarks: PF-PASCAL, PF-WILLOW, and SPair-71k. Our KBC method demonstrates a substantial performance improvement of 7.5\% on the SPair-71K dataset, providing compelling evidence of its efficacy. © 2024, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Nondestructive quantitative analysis of compound paracetamol and diphenhydramine hydrochloride powder using RBF networks

Nondestructive quantitative analysis of compound paracetamol...

引用

International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Zheng, Hong Wan, Li.-Ming Jiang, Jing-Qing Liang, Yan-Chun Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Computer Science and Engineering Changchun University of Technology Changchun 130012 China Institute of General Development and Evaluation of Equipment EAAF of PLA Beijing 100076 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China

ISBN: (纸本)078039092X

A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drugs on near-infrared (NIR) spectroscopy is developed by using a Radial Basis Function (RBF) network. Nearest neighbor-clustering algorithm is used as the learning algorithm of RBF network. Comparisons of the results obtained from the RBF models with those from BP models show that it is feasible to use the RBF network in nondestructive quantitative analysis of the components of drugs. © 2005 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Composing cardinal direction relations basing on interval algebra

Composing cardinal direction relations basing on interval al...

引用

作者： Chen, Juan Jia, Haiyang Liu, Dayou Zhang, Changhai College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)3642152791

Direction relations between extended spatial objects are important commonsense knowledge. Skiadopoulos proposed a formal model for representing direction relations between compound regions (the finite union of simple regions), known as SK-model. It perhaps is currently one of most cognitive plausible models for qualitative direction information, and has attracted interests from artificial intelligence and geographic information system. Originating from Allen first using composition table to process time interval constraints;composing has become the key technique in qualitative spatial reasoning to check the consistency. Due to the massive number of basic directions in SK-model, its composition becomes extraordinary complex. This paper proposed a novel algorithm for the composition. Basing the concepts of smallest rectangular directions and its original directions, it transforms the composition of basic cardinal direction relations into the composition of interval relations corresponding to Allen's interval algebra. Comparing with existing methods, this algorithm has quite good dimensional extendibility, that is, it can be easily transferred to the tridimensional space with a few modifications. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Algebra

来源：评论

学校读者我要写书评

暂无评论

Independently keypoint Learning for Small Object Semantic Correspondence

arXiv

引用

arXiv 2024年

Semantic correspondence remains a challenging task for establishing correspondences between a pair of images with the same category or similar scenes due to the large intra-class appearance. In this paper, we introduce a novel problem called’Small Object Semantic Correspondence (SOSC).’ This problem is challenging due to the close proximity of keypoints associated with small objects, which results in the fusion of these respective features. It is difficult to identify the corresponding key points of the fused features, and it is also difficult to be recognized. To address this challenge, we propose the keypoint Bounding box-centered Cropping (KBC) method, which aims to increase the spatial separation between keypoints of small objects, thereby facilitating independent learning of these keypoints. The KBC method is seamlessly integrated into our proposed inference pipeline and can be easily incorporated into other methodologies, resulting in significant performance enhancements. Additionally, we introduce a novel framework, named KBCNet, which serves as our baseline model. KBCNet comprises a Cross-Scale Feature Alignment (CSFA) module and an efficient 4D convolutional decoder. The CSFA module is designed to align multi-scale features, enriching keypoint representations by integrating fine-grained features and deep semantic features. Meanwhile, the 4D convolutional decoder, based on efficient 4D convolution, ensures efficiency and rapid convergence. To empirically validate the effectiveness of our proposed methodology, extensive experiments are conducted on three widely used benchmarks: PF-PASCAL, PF-WILLOW, and SPair-71k. Our KBC method demonstrates a substantial performance improvement of 7.5% on the SPair-71K dataset, providing compelling evidence of its efficacy. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Learning Entity and Relation Embeddings with Entity Description for knowledge Graph Completion

Learning Entity and Relation Embeddings with Entity Descript...

引用

2018 2nd International Conference on Artificial Intelligence: Technologies and Applications（ICAITA2018）

作者： Shaozhi Dai Yanchun Liang Shuyan Liu Ying Wang Wenle Shao Xixun Lin Xiaoyue Feng Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University

With the growth of existing knowledge graph, the completion of knowledge graph has become a crucial problem. In this paper, we propose a novel model based on descriptionembodied knowledge representation learning framework, which is able to take advantages of both fact triples and entity description. Specifically, the relation projection is combined with description-embodied representation learning to learn entity and relation embeddings. Convolutional neural network and Trans R are adopted to get the description-based and structure-based representation of entity and relation, respectively. We employ FB15 K dataset generated from a large knowledge graph freebase, to evaluate the performances of the proposed model. Experimental results show that our proposed model greatly outperforms other existing baseline models.

关键词： knowledge graph completion entity description natural language processing

来源：评论

学校读者我要写书评

暂无评论

Semantic retrieval system based on corn ontology

Semantic retrieval system based on corn ontology

引用

International Conference on Frontier of Computer Science and Technology

作者： Qi, Hong Zhang, Liangliang Gao, Ying Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China College of Computer Science and Technology Jilin University Alcatel-Lucent Shanghai Bell Co. Ltd. China

ISBN: (纸本)9780769541396

Most of the current information retrieval systems are mainly based on full text matching of keywords or topic-based classification, often return a large number of irrelevant information, and are unable to meet the user's request. Ontology-based semantic retrieval is a hot issue in current research. In this paper, the corn plant ontology is constructed using Formal Concept Analysis based approach in which the concept lattice is built from terminology-file relationship table and further reduced. Based on the corn plant ontology, we propose a semantic annotation method in which the feature words are selected by an improved method for weight calculation and the RDF triples are generated by syntactic parser. Finally a semantic retrieval system for corn plant is developed. In comparative experiment one hundred documents are selected as the dataset, and the result shows that the semantic retrieval system introduced in this paper is superior to keyword-based retrieval method in precision ratio and recall ratio. © 2010 IEEE.

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

Deep Feature-Based Text Clustering and Its Explanation

Deep Feature-Based Text Clustering and Its Explanation

引用

International Conference on Data engineering

作者： Renchu Guan Hao Zhang Yanchun Liang Fausto Giunchiglia Lan Huang Xiaoyue Feng Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China DISI University of Trento Trento Italy

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results. Our code is available at https://***/KEAML-JLU/DeepTextClustering.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Generalized Extended tanh-function Method for Traveling Wave Solutions of Nonlinear Physical Equations

引用

Communications in Mathematical Research 2014年第1期30卷 60-70页

作者： CHANG JING GAO YI-XIAN AND CAI HUA Fundamental Department Aviation University of Air Force College of Mathematics and Statistics Northeast Normal University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University School of Mathematics Jilin University

In this paper, the generalized extended tanh-function method is used for constructing the traveling wave solutions of nonlinear evolution equations. We choose Fisher＇s equation, the nonlinear schr＆#168;odinger equation to illustrate the validity and ad-vantages of the method. Many new and more general traveling wave solutions are obtained. Furthermore, this method can also be applied to other nonlinear equations in physics.

关键词： generalized tanh-function method nonlinear Schrodinger equation,Fisher＇s equation

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：