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Probabilistic rough-set-based band selection method for hyperspectral data classification 基于概率粗糙集的高光谱数据波段选择方法
IF 2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2019-01-01 DOI: 10.1504/ijcse.2019.10019529
Deng Shaobo, Wang Lei, Li Min
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引用次数: 0
A novel clustering algorithm based on the deviation factor model 一种基于偏差因子模型的聚类算法
IF 2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2019-01-01 DOI: 10.1504/IJCSE.2019.10022775
Chen Jungan, Chen Jinyin, Yang Dongyong
For classical clustering algorithms, it is difficult to find clusters that have non-spherical shapes or varied size and density. In view of this, many methods have been proposed in recent years to overcome this problem, such as introducing more representative points per cluster, considering both interconnectivity and closeness, and adopting the density-based method. However, the density defined in DBSCAN is decided by minPts and Eps, and it is not the best solution to describe the data distribution of one cluster. In this paper, a deviation factor model is proposed to describe the data distribution and a novel clustering algorithm based on artificial immune system is presented. The experimental results show that the proposed algorithm is more effective than DBSCAN, k-means, etc.
对于传统的聚类算法,很难找到具有非球形或大小和密度变化的聚类。鉴于此,近年来提出了许多方法来克服这一问题,例如在每个聚类中引入更多的代表性点,同时考虑互联性和紧密性,以及采用基于密度的方法。然而,DBSCAN中定义的密度是由minpt和Eps决定的,它不是描述一个集群的数据分布的最佳解决方案。本文提出了一种描述数据分布的偏差因子模型,并提出了一种基于人工免疫系统的聚类算法。实验结果表明,该算法比DBSCAN、k-means等算法更有效。
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引用次数: 4
Out-of-core streamline visualisation based on adaptive partitioning and data prefetching 基于自适应分区和数据预取的核外流线可视化
IF 2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2019-01-01 DOI: 10.1504/ijcse.2019.10021550
Li Sikun, W. Wenke, Guo Yumeng
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引用次数: 0
The intensional semantic conceptual graph matching algorithm based on conceptual sub-graph weight self-adjustment 基于概念子图权值自调整的内涵语义概念图匹配算法
IF 2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2018-02-02 DOI: 10.1504/IJCSE.2018.10010356
Xiong Li-yan, Zeng Hui, C. Jianjun
Semantic computing is an important task in the research on natural language processing. On solving the problem of the inaccurate conceptual graph matching, this paper proposes an algorithm to compute the similarity of conceptual graphs, based on conceptual sub-graph weight self-adjustment. The algorithm works by basing itself on the intensional logic model of Chinese concept connotation, using intensional semantic conceptual graph as knowledge representation method and combining itself with the computation method of E-A-V structures. When computing the similarity of conceptual graphs, the algorithm can give the homologous weight to the sub-graph according to the proportion of how much information the sub-graph contains in the whole conceptual graph. Therefore, it can achieve better similarity results, which has also been proved in the experiments of this paper.
语义计算是自然语言处理研究中的一个重要课题。针对概念图匹配不准确的问题,提出了一种基于概念子图权值自调整的概念图相似度计算算法。该算法以汉语概念内涵的内涵逻辑模型为基础,采用内涵语义概念图作为知识表示方法,结合E-A-V结构的计算方法。在计算概念图的相似度时,该算法可以根据子图所包含的信息量在整个概念图中所占的比例,给予子图相应的权重。因此,它可以获得更好的相似结果,这在本文的实验中也得到了证明。
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引用次数: 1
Collating multisource geospatial data for vegetation detection using Bayesian network-a case study of Yellow River Delta 基于贝叶斯网络的多源地理空间数据植被检测整理——以黄河三角洲为例
IF 2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2017-10-16 DOI: 10.1504/IJCSE.2017.087407
Dingyuan Mo, Liangju Yu, Meng Gao
Multisource geospatial data contains a lot of information that can be used for environment assessment and management. In this paper, four environmental indicators representing typical human activities in Yellow River Delta, China are extracted from multisource geospatial data. By analysing the causal relationship between these human-related indicators and NDVI, a Bayesian network (BN) model is developed. Part of the raster data pre-processed using GIS is used for training the BN model, and the other data is used for model testing. Sensitivity analysis and performance assessment showed that the BN model was good enough to reveal the impacts of human activities on land vegetation. With the trained BN model, the vegetation change under three different scenarios was also predicted. The results showed that multisource geospatial data could be successfully collated using the GIS-BN framework for vegetation detection.
多源地理空间数据包含大量可用于环境评价和管理的信息。本文从多源地理空间数据中提取了代表黄河三角洲典型人类活动的4个环境指标。通过分析这些人性化指标与NDVI之间的因果关系,建立了贝叶斯网络(BN)模型。利用GIS预处理的栅格数据一部分用于训练BN模型,另一部分用于模型测试。敏感性分析和性能评价表明,BN模型能够较好地揭示人类活动对陆地植被的影响。利用训练好的BN模型,对三种不同情景下的植被变化进行了预测。结果表明,利用GIS-BN框架进行植被检测可以成功地对多源地理空间数据进行整理。
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引用次数: 0
Vector Extrapolation Methods with Applications 矢量外推方法及其应用
IF 2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2017-09-30 DOI: 10.1137/1.9781611974966
A. Sidi
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引用次数: 40
Allocation of energy-efficient tasks in cloud using dynamic voltage frequency scaling 基于动态电压频率标度的云环境节能任务分配
IF 2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2017-01-01 DOI: 10.1504/ijcse.2017.10017137
S. K. Jena, B. Sahoo, S. Mishra, Sampa Sahoo, Akram Khan
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引用次数: 0
Topic-specific image indexing and presentation for MEDLINE abstract MEDLINE摘要的主题特定图像索引和呈现
IF 2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2017-01-01 DOI: 10.1504/IJCSE.2017.10016221
Ye Wang, L. Gong, Tian Bai, Lan Huang
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引用次数: 0
An algorithm for mining frequent closed itemsets with density from data streams 从数据流中挖掘具有密度的频繁闭项集的算法
IF 2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2016-05-05 DOI: 10.1504/IJCSE.2016.076217
Dai Caiyan, Chen Ling
Mining frequent closed itemsets from data streams is an important topic. In this paper, we propose an algorithm for mining frequent closed itemsets from data streams based on a time fading module. By dynamically constructing a pattern tree, the algorithm calculates densities of the itemsets in the pattern tree using a fading factor. The algorithm deletes real infrequent itemsets from the pattern tree so as to reduce the memory cost. A density threshold function is designed in order to identify the real infrequent itemsets which should be deleted. Using such density threshold function, deleting the infrequent itemsets will not affect the result of frequent itemset detecting. The algorithm modifies the pattern tree and detects the frequent closed itemsets in a fixed time interval so as to reduce the computation time. We also analyse the error caused by deleting the infrequent itemsets. The experimental results indicate that our algorithm can get higher accuracy results, and needs less memory and computation time than other algorithm.
从数据流中挖掘频繁闭项集是一个重要的课题。本文提出了一种基于时间衰落模块的数据流频繁闭项集挖掘算法。该算法通过动态构造模式树,利用衰落因子计算模式树中项目集的密度。该算法从模式树中删除真实的不频繁项集,以减少内存开销。设计了密度阈值函数,以识别需要删除的实际不频繁项集。利用该密度阈值函数,删除不频繁项集不会影响频繁项集检测的结果。该算法对模式树进行修改,以固定的时间间隔检测频繁闭合项集,从而减少计算时间。我们还分析了删除不频繁项集所引起的误差。实验结果表明,与其他算法相比,该算法可以获得更高的精度结果,并且需要更少的内存和计算时间。
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引用次数: 3
Pseudo Zernike moments based approach for text detection and localisation from lecture videos 基于伪泽尼克矩的演讲视频文本检测和定位方法
IF 2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2016-01-01 DOI: 10.1504/IJCSE.2016.10011674
Belkacem Soundes, Guezouli Larbi, Zidat Samir
Scene text presents challenging characteristics mainly related to acquisition circumstances and environmental changes resulting in low quality videos. In this paper, we present a scene text detection algorithm based on pseudo Zernike moments (PZMs) and stroke features from low resolution lecture videos. Algorithm mainly consists of three steps: slide detection, text detection and segmentation and non-text filtering. In lecture videos, slide region is a key object carrying almost all important information; hence slide region has to be extracted and segmented from other scene objects considered as background for later processing. Slide region detection and segmentation is done by applying pseudo Zernike moment's based on RGB frames. Text detection and extraction is performed using PZMs segmentation over V channel of HSV colour space, and then stroke feature is used to filter out non-text region and to remove false positives. The algorithm is robust to illumination, low resolution and uneven luminance from compressed videos. Effectiveness of PZM description leads to very few false positives comparing to other approached. Moreover resulting images can be used directly by OCR engines and no more processing is needed.
场景文本呈现出具有挑战性的特征,主要与获取环境和环境变化有关,导致视频质量低。在本文中,我们提出了一种基于伪泽尼克矩(PZMs)和笔画特征的低分辨率演讲视频场景文本检测算法。算法主要包括三个步骤:幻灯片检测、文本检测与分割和非文本过滤。在讲课视频中,幻灯片区域是承载几乎所有重要信息的关键对象;因此,必须从作为背景的其他场景对象中提取和分割滑动区域,以供后续处理。采用基于RGB帧的伪泽尼克矩进行滑动区域检测和分割。在HSV颜色空间的V通道上使用PZMs分割进行文本检测和提取,然后使用笔画特征过滤掉非文本区域并去除误报。该算法对压缩视频的光照、低分辨率和不均匀亮度具有较强的鲁棒性。与其他方法相比,PZM描述的有效性导致很少的误报。此外,生成的图像可以直接由OCR引擎使用,而不需要更多的处理。
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引用次数: 1
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International Journal of Computational Science and Engineering
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