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Underwater object detection by fusing features from different representations of sonar data Research Article

Fei WANG, Wanyu LI, Miao LIU, Jingchun ZHOU, Weishi ZHANG,feiwang@dlmu.edu.cn,zhoujingchun@dlmu.edu.cn,teesiv@dlmu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 6,   Pages 828-843 doi: 10.1631/FITEE.2200429

Abstract: Modern methods recognize objects from sonar data based on their geometric shapes.However, the distortion of objects during data acquisition and representation is seldom considered.In this paper, we present a detailed summary of representations for sonar data and a concrete analysison this, a framework is proposed to fully use the intensity features extracted from the polar image representationand the geometric features learned from the point cloud representation of sonar data.

Keywords： Underwater object detection     Sonar data representation     Feature fusion    

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Development and Prospect of Big Data Knowledge Engineering

Zheng Qinghua, Liu Huan, Gong Tieliang, Zhang Lingling, Liu Jun

Strategic Study of CAE 2023, Volume 25, Issue 2,   Pages 208-220 doi: 10.15302/J-SSCAE-2023.02.018

Abstract:

Big Data Knowledge Engineering is the infrastructure of artificial intelligenceIn this paper, we firstly elaborate on the background and connotation of big data knowledge engineeringand propose a research framework of “data knowledgeization, knowledge systematization, and knowledgeSecondly, we sort out the key technologies of knowledge acquisition and fusion, knowledge representationdirections including complex big data knowledge acquisition, knowledge+data hybrid learning, and brain-inspired

Keywords： Big Data Knowledge Engineering     Knowledge Acquisition     Knowledge Fusion     Knowledge Representation     Knowledge    

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Standard model of knowledge representation

Wensheng YIN

Frontiers of Mechanical Engineering 2016, Volume 11, Issue 3,   Pages 275-288 doi: 10.1007/s11465-016-0372-3

Abstract:

Knowledge representation is the core of artificial intelligence research.Knowledge representation methods include predicate logic, semantic network, computer programming languageTo establish the intrinsic link between various knowledge representation methods, a unified knowledgerepresentation model is necessary.This knowledge representation method is not a contradiction to the traditional knowledge representation

Keywords： knowledge representation     standard model     ontology     system theory     control theory     multidimensional representation    

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Multiple knowledge representation for big data artificial intelligence: framework, applications, and Perspective

Yi Yang, Yueting Zhuang, Yunhe Pan,yangyics@zju.edu.cn,yzhuang@zju.edu.cn,panyh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12,   Pages 1551-1684 doi: 10.1631/FITEE.2100463

Abstract: In this paper, we present a multiple knowledge representation (MKR) framework and discuss its potentialfor developing big data artificial intelligence (AI) techniques with possible broader impacts acrossMKR is an advanced AI representation framework for more complete intelligent functions, such as raw signaltechniques by integrating MKR to facilitate the mutual benefits of the complementary capacity of each representation

Keywords： 多重知识表达；人工智能；大数据    

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Applicability of high dimensional model representation correlations for ignition delay times of n-heptane

Wang LIU, Jiabo ZHANG, Zhen HUANG, Dong HAN

Frontiers in Energy 2019, Volume 13, Issue 2,   Pages 367-376 doi: 10.1007/s11708-018-0584-9

Abstract: In this paper, the random sampling-high dimensional model representation (RS-HDMR) methods were employed

Keywords： ignition delay     random sampling     high dimensional model representation     n-heptane     fuel kinetics    

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Digital representation of meso-geomaterial spatial distribution and associated numerical analysis of

YUE Zhongqi

Frontiers of Structural and Civil Engineering 2007, Volume 1, Issue 1,   Pages 80-93 doi: 10.1007/s11709-007-0008-0

Abstract: presents the author's efforts in the past decade for the establishment of a practical approach of digital representationproposed approach, digital image processing methods are used as measurement tools to construct a digital representationThe digital data are further processed to automatically generate meshes or grids for numerical analysis

Keywords： homogeneous     numerical analysis     Expanded     homogenization     meso-level    

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Uncertainty in Knowledge Representation

Li Deyi

Strategic Study of CAE 2000, Volume 2, Issue 10,   Pages 73-79

Abstract:

Knowledge representation in AI has been a bottleneck for years.The cloud models have been used in data mining, intelligent control, hopping frequency technique, system

Keywords： knowledge representation     qualitative concept     uncertainty     cloud model     digital characteristics    

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A discussion of objective function representation methods in global optimization

Panos M. PARDALOS, Mahdi FATHI

Frontiers of Engineering Management 2018, Volume 5, Issue 4,   Pages 515-523 doi: 10.15302/J-FEM-2018044

Abstract: We examine decomposition techniques and classify GO problems on the basis of objective function representationFinally, we conclude the paper by exploring the importance of objective function representation in integrated

Keywords： global optimization     decomposition techniques     multi-objective     DC programming     Kolmogorov’s superposition     space-filling curve     smart manufacturing and Industry 4.0    

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Syntactic word embedding based on dependency syntax and polysemous analysis None

Zhong-lin YE, Hai-xing ZHAO

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 524-535 doi: 10.1631/FITEE.1601846

Abstract: To solve these problems, we propose an easy-to-use representation algorithm of syntactic word embeddingThe main procedures are: (1) A polysemous tagging algorithm is used for polysemous representation by

Keywords： Dependency-based context     Polysemous word representation     Representation learning     Syntactic word embedding    

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Multiple Knowledge Representation of Artificial Intelligence

Yunhe Pan

Engineering 2020, Volume 6, Issue 3,   Pages 216-217 doi: 10.1016/j.eng.2019.12.011

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and unary classification

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 527-544 doi: 10.1007/s11708-023-0880-x

Abstract: Data-based methods of supervised learning have gained popularity because of available Big Data and computingand cannot process unlabeled data.The scarcity of fault data and a large amount of normal data in practical use pose great challenges toThe self-supervised representation learning uses a sequence-based Triplet Loss.The extracted features of large amounts of normal data are then fed into a unary classifier.

Keywords： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear    

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Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 488-497 doi: 10.1007/s11684-020-0762-0

Abstract: dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data

Keywords： knowledge representation     uncertain     causality     graphical model     artificial intelligence     diagnosis     dyspnea    

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Symbolic representation based on trend features for knowledge discovery in long time series

Hong YIN,Shu-qiang YANG,Xiao-qian ZHU,Shao-dong MA,Lu-min ZHANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 9,   Pages 744-758 doi: 10.1631/FITEE.1400376

Abstract: The symbolic representation of time series has attracted much research interest recently.The high dimensionality typical of the data is challenging, especially as the time series becomes longerThe wide distribution of sensors collecting more and more data exacerbates the problem.In this paper, we propose a new symbolic representation method for long time series based on trend features

Keywords： Long time series     Segmentation     Trend features     Symbolic     Knowledge discovery    

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A review of systematic evaluation and improvement in the big data environment

Feng YANG, Manman WANG

Frontiers of Engineering Management 2020, Volume 7, Issue 1,   Pages 27-46 doi: 10.1007/s42524-020-0092-6

Abstract: The era of big data brings unprecedented opportunities and challenges to management research.Exploring the applicable evaluation methods in the big data environment has become an important subjectpaper is to provide an overview and discussion of systematic evaluation and improvement in the big dataWe first review the evaluation methods based on the main analytic techniques of big data such as dataFocused on the characteristics of big data (association feature, data loss, data noise, and visualization

Keywords： big data     evaluation methods     systematic improvement     big data analytic techniques     data mining    

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Data quality assessment for studies investigating microplastics and nanoplastics in food products: Arecurrent data reliable?

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1694-0

Abstract:

● Data quality assessment criteria for MP/NPs in food products were

Keywords： Microplastic     Nanoplastic     Food products     Data quality     Human health risk    

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Title Author Date Type Operation

Underwater object detection by fusing features from different representations of sonar data

Fei WANG, Wanyu LI, Miao LIU, Jingchun ZHOU, Weishi ZHANG,feiwang@dlmu.edu.cn,zhoujingchun@dlmu.edu.cn,teesiv@dlmu.edu.cn

Journal Article

Development and Prospect of Big Data Knowledge Engineering

Zheng Qinghua, Liu Huan, Gong Tieliang, Zhang Lingling, Liu Jun

Journal Article

Standard model of knowledge representation

Wensheng YIN

Journal Article

Multiple knowledge representation for big data artificial intelligence: framework, applications, and

Yi Yang, Yueting Zhuang, Yunhe Pan,yangyics@zju.edu.cn,yzhuang@zju.edu.cn,panyh@zju.edu.cn

Journal Article

Applicability of high dimensional model representation correlations for ignition delay times of n-heptane

Wang LIU, Jiabo ZHANG, Zhen HUANG, Dong HAN

Journal Article

Digital representation of meso-geomaterial spatial distribution and associated numerical analysis of

YUE Zhongqi

Journal Article

Uncertainty in Knowledge Representation

Li Deyi

Journal Article

A discussion of objective function representation methods in global optimization

Panos M. PARDALOS, Mahdi FATHI

Journal Article

Syntactic word embedding based on dependency syntax and polysemous analysis

Zhong-lin YE, Hai-xing ZHAO

Journal Article

Multiple Knowledge Representation of Artificial Intelligence

Yunhe Pan

Journal Article

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and unary classification

Journal Article

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

Journal Article

Symbolic representation based on trend features for knowledge discovery in long time series

Hong YIN,Shu-qiang YANG,Xiao-qian ZHU,Shao-dong MA,Lu-min ZHANG

Journal Article

A review of systematic evaluation and improvement in the big data environment

Feng YANG, Manman WANG

Journal Article

Data quality assessment for studies investigating microplastics and nanoplastics in food products: Arecurrent data reliable?

Journal Article