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Improved SfM-Based Indoor Localization with Occlusion Removal 改进的基于sfm的室内定位与遮挡去除
Pub Date : 2018-07-01 DOI: 10.4018/IJSSCI.2018070102
Yushi Li, G. Baciu, Yu Han, Chenhui Li
This article describes a novel 3D image-based indoor localization system integrated with an improved SfM (structure from motion) approach and an obstacle removal component. In contrast with existing state-of-the-art localization techniques focusing on static outdoor or indoor environments, the adverse effects, generated by moving obstacles in busy indoor spaces, are considered in this work. In particular, the problem of occlusion removal is converted into a separation problem of moving foreground and static background. A low-rank and sparse matrix decomposition approach is used to solve this problem efficiently. Moreover, a SfM with RT (re-triangulation) is adopted in order to handle the drifting problem of incremental SfM method in indoor scene reconstruction. To evaluate the performance of the system, three data sets and the corresponding query sets are established to simulate different states of the indoor environment. Quantitative experimental results demonstrate that both query registration rate and localization accuracy increase significantly after integrating the authors' improvements.
本文介绍了一种新的基于三维图像的室内定位系统,该系统集成了改进的SfM(运动结构)方法和障碍物去除组件。与现有的专注于静态室外或室内环境的最先进定位技术相比,本工作考虑了繁忙室内空间中移动障碍物产生的不利影响。特别是,将遮挡去除问题转化为动态前景和静态背景的分离问题。采用低秩稀疏矩阵分解方法有效地解决了这一问题。此外,为了解决增量SfM方法在室内场景重建中的漂移问题,采用了一种带RT (re-triangulation)的SfM方法。为了评估系统的性能,建立了三个数据集和相应的查询集来模拟室内环境的不同状态。定量实验结果表明,综合作者的改进后,查询配准率和定位精度都有了显著提高。
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引用次数: 0
Application of Structural Properties of Seismic Data to Prediction of Hydrocarbon Distribution 地震资料结构性质在油气分布预测中的应用
Pub Date : 2018-07-01 DOI: 10.4018/IJSSCI.2018070103
Wei Zhou, Haimin Guo, Yaoting Lin
This article describes how under the influence of traps and trap ranges in size, final moisture content in oil production and changes in the reservoir is very large. Due to this, thin and dispersed oil concentration, facies changes and oil complexes, and strong segmentation, results in poor comparability between wells and conventional methods of seismic reservoir prediction has been more difficult to meet the development needs of the block. Therefore, the introduction of methods of seismic data structure characteristics, with a method based on sequence structure of underground rock formations, rock and petroleum allows the prediction for oil and gas purposes. Through the application of seismic structural properties, the result has been verified in practice and achieved a good application effect.
本文介绍了在圈闭和圈闭大小范围的影响下,最终含油量在采出和储层中的变化是非常大的。因此,油层稀分散、油相变化和油杂、分割性强,导致井间可比性差,常规地震储层预测方法更难满足区块开发需要。因此,引入地震数据结构特征的方法,采用基于地下岩层层序结构的方法,使岩石与石油的预测达到油气目的。通过对结构抗震性能的应用,结果在实践中得到了验证,取得了良好的应用效果。
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引用次数: 0
Jamming-Resilient Wideband Cognitive Radios with Multi-Agent Reinforcement Learning 基于多智能体强化学习的抗干扰宽带认知无线电
Pub Date : 2018-07-01 DOI: 10.4018/IJSSCI.2018070101
Mohamed A. Aref, S. Jayaweera
This article presents a design of a wideband autonomous cognitive radio (WACR) for anti-jamming and interference-avoidance. The proposed system model allows multiple WACRs to simultaneously operate over the same spectrum range producing a multi-agent environment. The objective of each radio is to predict and evade a dynamic jammer signal as well as avoiding transmissions of other WACRs. The proposed cognitive framework is made of two operations: sensing and transmission. Each operation is helped by its own learning algorithm based on Q-learning, but both will be experiencing the same RF environment. The simulation results indicate that the proposed cognitive anti-jamming technique has low computational complexity and significantly outperforms non-cognitive sub-band selection policy while being sufficiently robust against the impact of sensing errors.
本文提出了一种宽带自主认知无线电(WACR)的抗干扰和抗干扰设计。所提出的系统模型允许多个wacr在相同的频谱范围内同时运行,从而产生一个多智能体环境。每个无线电的目标是预测和躲避动态干扰信号以及避免其他wacr的传输。所提出的认知框架由两个操作组成:感知和传递。每个操作都有自己的基于q学习的学习算法,但它们都将经历相同的RF环境。仿真结果表明,所提出的认知抗干扰技术具有较低的计算复杂度,显著优于非认知子带选择策略,同时对感知误差的影响具有足够的鲁棒性。
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引用次数: 1
Dynamic Monitoring of Forest Volumes by a Feature Extraction Method 基于特征提取方法的森林体积动态监测
Pub Date : 2018-07-01 DOI: 10.4018/IJSSCI.2018070104
Xu Jie, Dawei Qi
In this article, in order to improve tree volume calculation method, a measurement method based on tree information point feature extraction is proposed, the method based on image processing and binocular vision, according to the measurement result of information point change and tree growth model, achieve through the distance change of information point to study the tree volume change. The visual measurement method is compared with the traditional method, the feasibility and accuracy of the method are proven. From the results, tree volume changes through the information point feature extraction and the traditional breast diameter measurement is very similar, the maximal percentage increase is 2.570% and 2.546%, the minimum percentage increase is 0.092% and 0.068%, which shows that volume change is consistent with the results, confirmed the tree volume change scheme of visual measurement is feasible and the result is reliable, which can reduce the impact of environmental change in the manual measurement.
本文为了改进树木体积计算方法,提出了一种基于树木信息点特征提取的测量方法,该方法基于图像处理和双目视觉,根据信息点变化的测量结果和树木生长模型,实现通过信息点的距离变化来研究树木体积变化。将目测方法与传统方法进行了比较,验证了目测方法的可行性和准确性。从结果来看,通过信息点特征提取的树木体积变化与传统的胸径测量非常相似,最大增加百分比分别为2.570%和2.546%,最小增加百分比分别为0.092%和0.068%,表明体积变化与结果一致,证实了目测树木体积变化方案的可行性和结果的可靠性。从而可以减少人工测量中环境变化的影响。
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引用次数: 1
A Fine-Grained Stateful Data Analytics Method Based on Resilient State Table 基于弹性状态表的细粒度状态数据分析方法
Pub Date : 2018-04-01 DOI: 10.4018/IJSSCI.2018040105
Jike Ge, Wenbo He, Zuqin Chen, Can Liu, Jun Peng, Guorong Chen
Thisarticledescribeshowstatefuldataanalyticframeworkshaveemergedtoprovidefreshandlowlatencyresultsforbigdataprocessing.Atpresent,itisdesiredtoachievethefine-graineddatamodel inSparkdataprocessingframework.However,Sparkadoptscoarse-graineddatamodelinorderto facilitateparallelization,itischallengingindealingwiththefine-graineddataaccessinstatefuldata analytics.Inthispaper,theauthorsintroduceafine-grainedstatefuldatacomponent,ResilientState Table(RST),toSparkframework.Forfillingthegapbetweenthecoarse-graineddatamodelinSpark andthefine-graineddataaccessrequirementsinstatefuldataanalytics,theydevisetheprogramming model of RST which interacts with Spark’s coarse-grained memory representation seamlessly, andenableuserstoquery/updatethestateentriesinfinegranularitywithSpark-likeprogramming interfaces.Performanceevaluationexperimentsinvariousapplicationfieldsdemonstratethattheir proposedsolutionachievestheimprovementsinlatency,fault-tolerance,aswellasscalability. KeywoRDS Big Data, Resilient Distributed Dataset, Resilient State Table, Spark, Stateful Data Analytics
Thisarticledescribeshowstatefuldataanalyticframeworkshaveemergedtoprovidefreshandlowlatencyresultsforbigdataprocessing。Atpresent,itisdesiredtoachievethefine-graineddatamodel inSparkdataprocessingframework。However,Sparkadoptscoarse-graineddatamodelinorderto facilitateparallelization,itischallengingindealingwiththefine-graineddataaccessinstatefuldata分析。Inthispaper,theauthorsintroduceafine-grainedstatefuldatacomponent,ResilientState表(RST),toSparkframework。Forfillingthegapbetweenthecoarse-graineddatamodelinSpark andthefine-graineddataaccessrequirementsinstatefuldataanalytics,theydevisetheprogramming与Spark的粗粒度内存表示无缝交互的rst_模型,andenableuserstoquery/updatethestateentriesinfinegranularitywithSpark-likeprogramming接口。Performanceevaluationexperimentsinvariousapplicationfieldsdemonstratethattheir proposedsolutionachievestheimprovementsinlatency,fault-tolerance,aswellasscalability。关键词:大数据,弹性分布式数据集,弹性状态表,Spark,有状态数据分析
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引用次数: 1
Nuclei Segmentation for Quantification of Brain Tumors in Digital Pathology Images 数字病理图像中量化脑肿瘤的核分割
Pub Date : 2018-04-01 DOI: 10.4018/IJSSCI.2018040103
P. Guo, A. Evans, P. Bhattacharya
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引用次数: 10
Passenger Condition Based Route-Planning for Cognitive Vehicle System 基于乘客状况的认知车辆系统路径规划
Pub Date : 2018-04-01 DOI: 10.4018/IJSSCI.2018040102
H. Hiraishi
Thisarticleproposesaroute-planningmethodforanenvironmentinwhichself-drivingvehiclesare widelyused.Suchvehiclesgenerateanewroutetoavoidtrafficcongestionwhenitoccurs.Through theself-developedtrafficsimulator,theauthorwasabletoclarifythatitisnotalwaysbesttogenerate anavoidanceroute,andthedecisiontodrivealongthecurrentroutewithoutgeneratinganavoidance routebecomesimportantincertaincases.Thus,theauthorproposesamethodinwhichavehiclejudges whethertogenerateanavoidanceroutebasedonthepassenger’scondition.Todetectthepassenger’s condition,theauthorusesasitting-pressuresensorandsucceededinrecognizingpassengerfatigue. Theauthorcanthereforemakecertainjudgments:Thevehiclewillgoalongthecurrentrouteifthe passengerseemstoberelaxedandinacomfortableatmosphere,thevehiclewillarriveearlierby avoidingtrafficcongestionifthepassengerseemstobetiredorirritated,orthevehiclewillstopfor abreakperiodifthepassengerseemstobesignificantlytired. KeywoRdS A* Search, Cognitive Route Search, Cognitive Vehicle System, Route-Planning, Self-Driving, Sitting-Pressure Sensor, Traffic Congestion, Traffic Simulation
Thisarticleproposesaroute-planningmethodforanenvironmentinwhichself-drivingvehiclesare widelyused.Suchvehiclesgenerateanewroutetoavoidtrafficcongestionwhenitoccurs。Through theself-developedtrafficsimulator,theauthorwasabletoclarifythatitisnotalwaysbesttogenerate anavoidanceroute,andthedecisiontodrivealongthecurrentroutewithoutgeneratinganavoidance routebecomesimportantincertaincases。Thus,theauthorproposesamethodinwhichavehiclejudges whethertogenerateanavoidanceroutebasedonthepassenger 'scondition。Todetectthepassenger的情况,theauthorusesasitting-pressuresensorandsucceededinrecognizingpassengerfatigue。Theauthorcanthereforemakecertainjudgments:Thevehiclewillgoalongthecurrentrouteifthe passengerseemstoberelaxedandinacomfortableatmosphere,thevehiclewillarriveearlierby avoidingtrafficcongestionifthepassengerseemstobetiredorirritated,orthevehiclewillstopfor abreakperiodifthepassengerseemstobesignificantlytired。关键词:A*搜索,认知路径搜索,认知车辆系统,路径规划,自动驾驶,坐位压力传感器,交通拥堵,交通仿真
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引用次数: 12
Investigations on the Brain Connectivity Parameters for Co-Morbidities of Autism Using EEG 自闭症共病脑连接参数的脑电图研究
Pub Date : 2018-04-01 DOI: 10.4018/IJSSCI.2018040104
K. Priya, A. Kavitha
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引用次数: 5
Discovering Attribute-Specific Features From Online Reviews: What Is the Gap Between Automated Tools and Human Cognition? 从在线评论中发现属性特定的特征:自动化工具和人类认知之间的差距是什么?
Pub Date : 2018-04-01 DOI: 10.4018/IJSSCI.2018040101
X. Jing, Penghao Wang, Julia M. Rayz
Thisarticledescribeshowonlinereviewsplayanimportantroleindatadrivendecisionmaking. Manyeffortshavebeeninvestedindeterminingtheoverallsentimentcarriedbythereviews.However, oftentimes,theoverallratingsofthereviewsdonotrepresentopinionstowardspecificattributes ofaproduct.Anidealopinionminingtoolshouldaimatfindingboththeproductattributesand theircorrespondingopinions.Theauthorsproposeanapproachforextractingtheattributespecific featuresfromonlinereviewsusingaWord2Vecmodelcombinedwithclustering.Twoexperiments aredescribed in thispaper: thefirst focuseson testing theperformanceof theWord2Vecmodel onextractingproductaspectwords,thesecondaddresseshowwelltheextractedfeaturesobtained arerecognizablebyhumancognition.Anewmetriccalledthe“splitvalue”thatisbasedoncluster similarityanddiversityisintroducedtoexaminetheconsistencyofclusteringalgorithm.Theauthors’ experimentssuggestthatmeaningfulclusters,whichprovideinsightstotheproductattributesand sentiments,couldbeextractedfromthereviews. KeyWORDS Artificial Intelligence, Clustering, Cognition, Feature Extraction, Opinion Mining, Text Understand, Word2Vec
Thisarticledescribeshowonlinereviewsplayanimportantroleindatadrivendecisionmaking。Manyeffortshavebeeninvestedindeterminingtheoverallsentimentcarriedbythereviews。However,通常是theoverallratingsofthereviewsdonotrepresentopinionstowardspecificattributes ofaproduct。Anidealopinionminingtoolshouldaimatfindingboththeproductattributesand theircorrespondingopinions。Theauthorsproposeanapproachforextractingtheattributespecific featuresfromonlinereviewsusingaWord2Vecmodelcombinedwithclustering。Twoexperiments aredescribed in> thispaper: > thefirst focuseson testing> theperformanceof theWord2Vecmodel onextractingproductaspectwords,thesecondaddresseshowwelltheextractedfeaturesobtained arerecognizablebyhumancognition。Anewmetriccalledthe " splitvalue " thatisbasedoncluster similarityanddiversityisintroducedtoexaminetheconsistencyofclusteringalgorithm。Theauthors ' ' experimentssuggestthatmeaningfulclusters,whichprovideinsightstotheproductattributesand情绪,couldbeextractedfromthereviews。关键词:人工智能,聚类,认知,特征提取,意见挖掘,文本理解,Word2Vec
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引用次数: 6
Human Identification Using Gait Skeletal Joint Distance Features 基于步态骨骼关节距离特征的人体识别
Pub Date : 2017-10-01 DOI: 10.4018/IJSSCI.2017100102
Md Wasiur Rahman, M. Gavrilova
Gaitnotonlydefinesthewayapersonwalks,butalsoprovidesinsightsonanindividual’sdaily routine,mentalstateorevencognitivefunction.Theimportanceofincorporatingcognitivebehavior andanalysisinbiometricsystemshasbeennotedrecently.Inthisarticle,authorsdevelopabiometric securitysystemusinggait-basedskeletalinformationobtainedfromMicrosoftKinectv1sensor.The gaitcycleiscalculatedbydetectingthethreeconsecutivelocalminimabetweenthejointdistance ofleftandrightankles.Authorshaveutilizedthedistancefeaturevectorforeachofthejointswith respecttootherjointsinthegaitcycle.Aftermeanandvariancefeaturesareextractedfromthedistance featurevector,theKNNalgorithmisusedforclassificationpurpose.Theclassificationaccuracyofthe authors’approachis93.33%.Experimentalresultsshowthattheproposedapproachachievesbetter recognitionaccuracythenotherstate-of-the-artapproaches.Incorporatinggaitbiometricinasituation awarenesssystemforidentificationofamentalstateisoneofthefuturedirectionsofthisresearch. KeywoRDS Biometric System, Cognitive Function, Feature Distance Vector, Gait, Gait Cycle, K Nearest Neighbors (KNN), Kinect Sensor, Pattern Recognition
Gaitnotonlydefinesthewayapersonwalks,butalsoprovidesinsightsonanindividual 'sdaily routine,mentalstateorevencognitivefunction。Theimportanceofincorporatingcognitivebehavior andanalysisinbiometricsystemshasbeennotedrecently。Inthisarticle,authorsdevelopabiometric securitysystemusinggait-basedskeletalinformationobtainedfromMicrosoftKinectv1sensor。The gaitcycleiscalculatedbydetectingthethreeconsecutivelocalminimabetweenthejointdistance ofleftandrightankles。Authorshaveutilizedthedistancefeaturevectorforeachofthejointswith respecttootherjointsinthegaitcycle。Aftermeanandvariancefeaturesareextractedfromthedistance featurevector,theKNNalgorithmisusedforclassificationpurpose。Theclassificationaccuracyofthe作者approachis93.33%。Experimentalresultsshowthattheproposedapproachachievesbetter recognitionaccuracythenotherstate-of-the-artapproaches。Incorporatinggaitbiometricinasituation awarenesssystemforidentificationofamentalstateisoneofthefuturedirectionsofthisresearch。关键词:生物识别系统,认知功能,特征距离向量,步态,步态周期,K近邻,Kinect传感器,模式识别
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引用次数: 2
期刊
Int. J. Softw. Sci. Comput. Intell.
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