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Image Processing on Geological data in Vector Format and multi-source Spatial data fusion

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Journal of China University of Geosciences 2003年第3期14卷 278-282页

作者： Liu Xing Hu Guangdao Qiu Yubao Faculty of Earth Resources, China University of Geosciences, Wuhan 430074 Faculty of Earth Resources China University of Geosciences Wuhan 430074

The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper converts the vector data into 8 bit images according to their importance to mineralization each by programming. We can communicate the geological meaning with the raster images by this method. The paper also fuses geographical data and geochemical data with the programmed strata data. The result shows that image fusion can express different intensities effectively and visualize the structure characters in 2 dimensions. Furthermore, it also can produce optimized information from multi-source data and express them more directly.

关键词： geological data GIS-based vector data conversion image processing multi-source data fusion

Study on Chicken Quality Classification Method Based on K-means-RBF multi-source data fusion

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Study on Chicken Quality Classification Method Based on K-me...

Threat Modeling and Application Research Based on multi-source Attack and Defense Knowledge

第30届中国控制与决策会议

作者： XING SuXia WANG Rui WANG JiuQing GUO PeiYuan College of Computer and Information Engineering Beijing Technology and Business University

In order to realize the rapid and non-destructive testing of chicken quality in broiler storage and transportation,the characteristics of hyperspectral imaging technology was used for the map and spectrum combo features,to extract the spectral characteristics,which that reflect the intrinsic quality of chicken;and the image texture features,color features,which reflect the external characteristics of chicken were also extracted.The multi-source data fusion method was used to establish K-means-RBF prediction model of chicken meat quality rapid classification.The results show that the multi-source data fusion detection model established by K-means-RBF neural network has higher prediction accuracy than single feature classification model,and the precision is up to 100%.It is proved that the validity and necessity of multi-source data fusion method in chicken quality testing.

关键词： chicken hyperspectral multi-source data fusion neural network

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Computers, Materials & Continua 2023年第10期77卷 349-377页

作者： Shuqin Zhang Xinyu Su Peiyu Shi Tianhui Du Yunfei Han School of Computer Science Zhongyuan University of TechnologyZhengzhouChina

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

关键词： multi-source data fusion threat modeling threat propagation path knowledge graph intelligent defense decision-making

Application Research on Two-Layer Threat Prediction Model Based on Event Graph

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Computers, Materials & Continua 2023年第12期77卷 3993-4023页

作者： Shuqin Zhang Xinyu Su Yunfei Han Tianhui Du Peiyu Shi School of Computer Science Zhongyuan University of TechnologyZhengzhouHEN037China

Advanced Persistent Threat(APT)is now the most common network assault.However,the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks.They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats.To address the above problems,firstly,this paper constructs the multi-source threat element analysis ontology(MTEAO)by integrating multi-source network security knowledge bases.Subsequently,based on MTEAO,we propose a two-layer threat prediction model(TL-TPM)that combines the knowledge graph and the event graph.The macro-layer of TL-TPM is based on the knowledge graph to derive the propagation path of threats among devices and to correlate threat elements for threat warning and decision-making;The micro-layer ingeniously maps the attack graph onto the event graph and derives the evolution path of attack techniques based on the event graph to improve the explainability of the evolution of threat events.The experiment’s results demonstrate that TL-TPM can completely depict the threat development trend,and the early warning results are more precise and scientific,offering knowledge and guidance for active defense.

关键词： Knowledge graph multi-source data fusion network security threat modeling event graph absorbing Markov chain threat propagation path

Deep learning-based prediction of effluent quality of a constructed wetland

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Environmental Science and Ecotechnology 2023年第1期13卷 64-74页

作者： Bowen Yang Zijie Xiao Qingjie Meng Yuan Yuan Wenqian Wang Haoyu Wang Yongmei Wang Xiaochi Feng State Key Laboratory of Urban Water Resource and Environment School of Civil and Environmental EngineeringHarbin Institute of Technology(Shenzhen)ShenzhenGuangdong518055PR China Shenzhen Shenshui Water Resources Consulting CO LTDShenzhenGuangdong518022PR China College of Biological Engineering Beijing PolytechnicBeijing10076PR China State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhen518055China

data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands.However,the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction.To address this problem,in this study,we propose an approach based on multi-source data fusion that considers the following indicators:water quality indicators,water quantity indicators,and meteorological indicators.In this study,we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland:(1)multiple linear regression;(2)backpropagation neural network(BPNN);(3)genetic algorithm combined with the BPNN to solve the local minima problem;and(4)long short-term memory(LSTM)neural network to consider the influence of past results on the present.The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method,with a satisfactory R^(2).Additionally,given the huge fluctuation of different pollutant concentrations in the effluent,we used a moving average method to smooth the original data,which successfully improved the accuracy of traditional neural networks and hybrid neural networks.The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.

关键词： LSTM Constructed wetlands Water quality prediction Deep learning multi-source data fusion

Design and Realization of Block Level Augmented Reality Three-Dimensional Map

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Journal of Computer and Communications 2023年第6期11卷 113-121页

作者： Dashuai Shang Chenguang Dai Ying Yu Yabing Fan Geospatial Information Institute of the Information Engineering University Zhengzhou China 61363 Troops Xi’an China

In response to the construction needs of “Real 3D China”, the system structure, functional framework, application direction and product form of block level augmented reality three-dimensional map is designed. Those provide references and ideas for the later large-scale production of augmented reality three-dimensional map. The augmented reality three-dimensional map is produced based on skyline software. Including the map browsing, measurement and analysis and so on, the basic function of three-dimensional map is realized. The special functional module including housing management, pipeline management and so on is developed combining the need of residential quarters development, that expands the application fields of augmented reality three-dimensional map. Those lay the groundwork for the application of augmented reality three-dimensional map. .

关键词： Augmented Reality Three-Dimensional Map multi-source data fusion Three-Dimensional Analysis Three-Dimensional Scene Skyline

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多源数据融合的南海领域主题词表

图书馆学情报学 2023年第1期

作者：齐小英丁子仪杨海平

以南海历史事件、新闻数据及文献资料为语料库，围绕南海领域特征，设计了基于分面分类法的主题词表顶层框架，从主体、物体、行为、资源、主题以及空间六个维度对南海领域主题词表进行概念描述；随后，通过候选词抽取、分类、筛选和主... 详细信息

以南海历史事件、新闻数据及文献资料为语料库，围绕南海领域特征，设计了基于分面分类法的主题词表顶层框架，从主体、物体、行为、资源、主题以及空间六个维度对南海领域主题词表进行概念描述；随后，通过候选词抽取、分类、筛选和主题词确定及词间关系识别，构建了南海领域主题词表。该词表梳理了领域内核心概念间的内在逻辑与知识脉络，为南海文献资源的语义分析提供组织工具与关联基础。图6。表10。参考文献43。

关键词：南海主题词表知识组织多源数据融合 South China Sea Thesaurus Knowledge Organization multi-source data fusion

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多源数据驱动的产业竞争情报智慧服务研究