Belief exponential divergence for D-S evidence theory and its application in multi-source information fusion

Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this issue,a fusion approach based on a newly defined belief exponential diver-gence and Deng entropy is proposed.First,a belief exponential divergence is proposed as the conflict measurement between evidences.Then,the credibility of each evidence is calculated.Afterwards,the Deng entropy is used to calculate information volume to determine the uncertainty of evidence.Then,the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence.Ultimately,initial evidences are amended and fused using Dempster’s rule of combination.The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic exam-ples.Additionally,the proposed approach is applied to aerial tar-get recognition and iris dataset-based classification to validate its efficacy.Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.

Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.

Fault location of distribution networks based on multi-source information

In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.

A multi-source information fusion method for tool life prediction based on CNN-SVM

For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.

Quantizing and analyzing the feature information of coastal zone based on high-resolution remote sensing image

On the basis of realization of beach information and its differentiating of high-resolution remote sensing image on coastal zone, extracting objects are carried through RS multi-scale diagnostic analysis, and fast information extraction methods and key technologies are put forward. Meanwhile image segmentation methods are set forth for objects of coastal zone. And through the application of Otsu2D to the segmentation of water area and dock and the applying of Gabor filter to the separation and extraction of construction, some typical applications of high-resolution RS image are presented in the field of coastal zone surface objects＇ recognition. Quantizing high-resolution RS information on the coastal zone proved to be of great scientific and practical significance for coastal development and management.

Research on extracting method of micro-scale remote sensing information combination and application in coastal zone

Due to the need of rapid and sustainable development in China’s coastal zones, the high-resolution information theory using data mining technology becomes an urgent research focus. However, the traditional pixel-based image analysis methods cannot meet the needs of this development trend. The paper attempts to present an information extraction approach in terms of image segmentation based on an object-oriented algorithm for high-resolution remote sensing images. An aim of the author’ research is to establish an identification system of ＂pixel-primitive-object＂. Through extraction and combination of micro-scale coastal zone features, some objects are classified or recognized, e.g., tidal flat, water line, sea wall, and mariculture pond. Firstly, the authors extract various internal features of relatively homogeneous primitive objects using an image segmentation algorithm based on both spectral and shape information. Secondly, the features of those primitives are analyzed to ascertain an optimal object by adopting certain feature rules. The results from this research indicate that our model is practical to realize and the extraction accuracy of the coastal information is significantly improved as compared with the traditional approaches. Therefore, this study provides a potential way to serve the author’ highly dynamic coastal zones for monitoring, management, development and utilization.

Construction and implementation of RS integrated application information system of China's coastal zone and offshore

The coastal zone and offshore are clearly of major economic and social importance, in thesame time it causes a series of problems of resources and ecosystem too. The research on and development of integrated application techniques for remote sensing provide not only a microcosmic and dynamic and synchronous technical means to the monitor, but also an integrated technical scheme to harmonically solve the ecological environment problem. The system is designed to focus on the application techniques of multi-sources remote sensing data. By developing remote sensing information extraction module, integrated user platform, and application module objected to the real ocean procedure for China' s coastal zone and offshore, the information system consists of the management of prodigious amount of data, display, analysis, simulation and output will be constructed and implemented. The final objective is to transform the research on ocean remote sensing into application.

Factors Influencing the Successful Adoption of Human Resource Information System: The Content of Aqaba Special Economic Zone Authority

This study examines the key factors that have impact on the successful adoption of Human Resource Information System (HRIS) within the Aqaba Special Economic Zone Authority (ASEZA)/Jordan. In order to accomplish the purpose of the study four critical factors are inquired. So, four critical factors are inquired: First, TAM Model (Perceived Ease of Use (PEOU) and Perceived Usefulness (PU)). Second, Information Technology Infrastructure (ITI). Third, Top Management Support (TMS). Finally, Individual Experience with Computer (IEC). The research model was applied to collect data from the questionnaires answered by 45 users of HRIS as a source of primary data, based on a convenience sample the response rate was about 91%. In addition, the results were analyzed by utilizing the Statistical Package for Social Software (SPSS). Furthermore, the findings were analyzed;multiple Regression analysis indicated that all research variables have significant relationship on successful adoption of HRIS. The findings indicated IT infrastructures have a positive and significant effect on the successful adoption of HRIS. But there is no significant of PU, PEOU, TMS, and IEC on the successful adoption of HRIS. Finally, the results indicated that no significant statistical differences of demographic characteristics on HRIS adoption. Depending on the research’s findings;the researchers proposed a set of recommendations for better adoption of HRIS in SEZA.

Structural damage detection method based on information fusion technique

Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.

Information freshness optimization of multiple status update streams in Internet of things:Generation rate control and service rate reservation

The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.

Assessment of Agricultural Drought Risk in the Sichuan Basin Based on Information Diffusion Theory

Based on data of agricultural drought situation and sown area of main crops in each county or district of the Sichuan Basin, the spatial distribution and probability of agricultural drought risk at different risk levels were studied using normal information diffusion method, and the risk zoning was carried out. The results showed that normal information diffusion method could fit the distribution of agricultural drought risk in the Sichuan Basin. By comparison with the end of the 20^th century, agricultural drought risk in Meishan, Chongqing City and so on increased at the beginning of the 21^st century when x1≥ 10% or x1≥40%. Agricultural drought risk was low in the west of the Sichuan Basin, which was related to rich precipitation here, but it was high in Bazhong, Zhongjiang, Luxian and so forth. The risk zoning results can provide scientific references for disaster prevention and emergency management of government.

暴雨灾害风险评估与区划——以山东省滨州市为例

选取滨州市国家级气象观测站1978~2020年观测资料及区域自动站建站以来的观测资料、暴雨灾害灾情、地理信息与其他社会经济资料,应用GIS技术及统计方法多因子的叠加,综合得出暴雨灾害风险评估和区划:(1)山东滨州暴雨危险性大致为中北部地区高于南部地区。(2)滨城区大部、邹平市中部偏南的地区、无棣县西南部、沾化区东南部、惠民县东部与南部边缘、阳信县中东部及博兴县南部边缘地区人口暴雨灾害风险为高或较高。(3)经济暴雨灾害风险高或者较高的地区主要为滨城区大部、邹平市中南部、惠民县北部与南部边缘、无棣县中南部及博兴县南部边缘地区。(4)滨城区中北部与南部边缘地区、惠民县东部和东南部、博兴县南部小部分地区、沾化中西部和东南部暴雨农业风险为高或较高。

应用GIS模糊层次分析法对森林火灾风险区划——以湖南省宁远县为例

应用ArcGIS的模糊逻辑和层次分析法,对湖南省宁远县的森林火灾风险进行评估和火险区划研究,参与评估的火险因子包括环境、人类活动和地形因子。研究结果表明:温度、降水、海拔和归一化植被指数(NDVI)对研究区域林火风险的影响最为显著;宁远县47.6%的区域被确定为四级及以上火险等级,主要分布于该县中部的大部分地区,该县火险情况严峻;编制的森林火灾风险图预测精度达82.7%。模糊层次分析法在森林火灾风险预测中展现出较好的效果,可作为林火预测和火险制图的重要工具,编制的森林火灾风险图为宁远县的林火管理提供了科学参考依据。

Delineation of groundwater potential zones in Theni district,Tamil Nadu,using remote sensing,GIS and MIF techniques

Integration of remote sensing data and the geographical information system （GIS） for the exploration of groundwater resources has become a breakthrough in the field of groundwater research, which assists in assessing, monitoring, and conserving groundwater resources. In the present paper, various groundwater potential zones for the assessment of groundwater availability in Theni district have been delineated using remote sensing and GIS techniques. Survey of India toposheets and IRS-IC satel- lite imageries are used to prepare various thematic layers viz. lithology, slope, land-use, lineament, drainage, soil, and rainfall were transformed to raster data using feature to raster converter tool in ArcGIS. The raster maps of these factors are allocated a fixed score and weight computed from multi influencing factor （MIF） technique. Moreover, each weighted thematic layer is statistically computed to get the groundwater potential zones. The groundwater potential zones thus obtained were divided into four categories, viz., very poor, poor, good, and very good zones. The result depicts the groundwater potential zones in the study area and found to be helpful in better planning and management of ground- water resonrces.

基于Zone模型的森林小班变化信息提取方法研究

利用遥感图像监测森林覆盖及其变化是遥感应用的重要领域之一。本研究基于建德市2013和2014年Landsat8 OLI遥感影像数据、森林资源二类调查小班数据及林地落界数据。采用统计分布和Zone模型两种方法提取有林地小班变化信息。评价结果表明:基于统计分布法操作简单但效果不够理想;基于Zone模型法检测结果精度高于基于统计分布法,但存在较多伪变化,正确检测率为80.58%,漏检率为19.42%,错检率为84.89%,是一种较好的提取有林地小班变化信息的方法。

Construction and implementation of multisource spatial data management system of China's coastal zone and offshore

To meet the increasing demand of national spatial database infrastructure construction and application, a concept model of China's coastal zone scientific data platform is established based on the information feature analysis of a compound dataset, consisting of remote sensing data and conventional data. Based on this concept model, the detailed logical database structure and the storage strategy of remote sensing data and their metadata using ArcSDE are designed. The complicated technology of multisources data combination in this research is crucial to the future coastal zone and offshore database construction and practical running, which will provide intelligent information analysis and technological service for coastal zone and offshore investigation, research, development and management.

Enhancing train position perception through Al-driven multi-source information fusion

This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.

The integrated risk assessment and zoning of meteorological disaster in Heilongjiang Province of China

This paper describes studies on the integrated risk assessment and zoning of meteorological disaster in Heilongjiang Province,in northeastern China,by using information-diffusion theory and cluster method with 35 years of summer temperature and precipitation data from 74 meteorological stations from 1971 through 2005.The information-diffusion theory has been used extensively in risk assessment,yet almost no one has done research about risk assessment by information-diffusion theory based on meteorological disaster standards.Some research results are as follows:the risk probability of low temperature and cold damage in the northern region is higher than that in the southern region;the risk probability of general low temperature and cold damage in the southwestern region is the highest;the risk probability of serious low temperature and cold damage in the northern region is the highest,followed by the central and southeast region;the high-risk region of arid disaster in Heilongjiang Province was primarily located in the southwestern,central,and southern parts of the province;the high-intensity arid disaster was located in the south-eastern region;the high-risk region of flood in Heilongjiang Province was primarily located from the southwest and then across the central part to the western part of Heilongjiang Province;the high-intensity flood disasters were located in almost every part of Heilongjiang Province.We can conclude from the integrated meteorological disaster risk zoning that the high-risk region of mete-orological disaster is primarily located in the southern and northern part of the province,the moderate-risk region is distributed in the central southern region and western region,the low-risk region is located in the eastern part,and the light-risk region is located in the central western part of Heilongjiang Province.

Integration Technique of Multi-source Information Dominated by Aerial Radiometric Measure-ment and Its Application

This paper aims at exploring a digital image integration technique for multi-geoscience in formation dominated by airborne gamma-ray data, especially deeply discussing the method to secondly develop those aerial data by combining digital image processing system with the colored mapping system. Utilizing this technique , we have analyzed the geologic environment of uranium mineralization of Lianshanguan area > Liaoning Province, provided some important background information for further seeking of minerals. Meanwhile , experimental studies have been made to predict uranium mineralization , and evident results aquired. Practise shows that this new technique offers prospecting significance for mineral seeking and great practical value in survey of uranium resources.

Mapping of groundwater potential zones in the drought-prone areas of south Madagascar using geospatial techniques

The southern regions of Madagascar have the country’s lowest water supply coverage and are highly vulnerable to drought.Access to potable drinking water is a major challenge for the local population.Chronic droughts lead to annual emergency appeals to save the lives of acute malnourished children.UNICEF’s response consisting in providing potable drinking water through the drilling of boreholes has been challenged by the complex hydrogeology,the low yield of boreholes and high-level salinity of water,the lack of reliable groundwater data and the weak capacity of the drilling sector.These constraints result in a high rate of drilling failure.To improve drilling success and provide more potable drinking water to local communities,it is vital to undertake reliable groundwater investigation.UNICEF Madagascar and the European Union delegation in Madagascar collaborated on the use of satellite imagery to improve sector knowledge and access to safe and clean water for local communities in southern Madagascar.The methodology relies on produce thematic layers of groundwater potential areas.Later,these thematic layers were overlaid with ground-based hydrogeological data to map the groundwater potential zones(GWP) and identify the most suitable sites for borehole siting and drilling.Findings of this study are very encouraging,and the integrated approach used has proven its applicability in mapping groundwater potential areas in the eight drought-affected areas of south Madagascar.The groundwater potential zone map is being used by UNICEF and partners to plan water supply projects and identify the best sites for positioning new boreholes and reduce the likelihood of drilling failure.Additionally,the project developed a database of groundwater resources,which will improve knowledge of the regional hydrogeological context and strengthen the capacity of the water sector.Lessons learnt from this study show that an integration of the groundwater potential zone map with demographics and water demand information will help identifying priority areas for detailed studies.Moreover,a capacity building activity is required for knowledge/technology transfer to the Ministry of Energy,Water and Hydrocarbons(MEEH),allowing the possibility of scaling-up this integrated approach to the rest of Madagascar.Finally,strengthening the capacity of the MEEH and refining this approach as suggested above will certainly help in the pursuit to improve equitable access to safe and clean water for households located in the drought-affected areas of southern Madagascar,allowing them to be more resilient to the effects of climate change.