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机器智能研究（英文）
机器智能研究（英文）

谭铁牛　刘国平　胡豁生

双月刊

2731-538X

ijac@ia.ac.cn

010-62655893

100190

北京海淀区中关村东路95号2728信箱

机器智能研究（英文）/Journal Machine Intelligence ResearchCSCDCSTPCD北大核心EI
查看更多>>International Journal of Automation and computing is a publication of Institute of Automation, the Chinese Academy of Sciencs and Chinese Automation and computing Society in the United Kingdom. The Journal publishes papers on original theoretical and experimental research and development in automation and computing. The scope of the journal is extensive. Topics include; artificial intelligence, automatic control, bioinformatics, computer sciene, information technology, modeling and simulation, networks and communications, optimization and decision, pattern recognition, robotics, signal processing, and systems engineering.
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    Comprehensive Relation Modelling for Image Paragraph Generation

    Xianglu ZhuZhang ZhangWei WangZilei Wang...
    369-382页
    查看更多>>摘要：Image paragraph generation aims to generate a long description composed of multiple sentences,which is different from tra-ditional image captioning containing only one sentence.Most of previous methods are dedicated to extracting rich features from image regions,and ignore modelling the visual relationships.In this paper,we propose a novel method to generate a paragraph by modelling visual relationships comprehensively.First,we parse an image into a scene graph,where each node represents a specific object and each edge denotes the relationship between two objects.Second,we enrich the object features by implicitly encoding visual relationships through a graph convolutional network(GCN).We further explore high-order relations between different relation features using anoth-er graph convolutional network.In addition,we obtain the linguistic features by projecting the predicted object labels and their relation-ships into a semantic embedding space.With these features,we present an attention-based topic generation network to select relevant features and produce a set of topic vectors,which are then utilized to generate multiple sentences.We evaluate the proposed method on the Stanford image-paragraph dataset which is currently the only available dataset for image paragraph generation,and our method achieves competitive performance in comparison with other state-of-the-art(SOTA)methods.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    CASIA-Iris-Africa:A Large-scale African Iris Image Database

    Jawad MuhammadYunlong WangJunxing HuKunbo Zhang...
    383-399页
    查看更多>>摘要：Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes.Re-search on iris biometrics has progressed tremendously,partly due to publicly available iris databases.Various databases have been avail-able to researchers that address pressing iris biometric challenges such as constraint,mobile,multispectral,synthetics,long-distance,contact lenses,liveness detection,etc.However,these databases mostly contain subjects of Caucasian and Asian docents with very few Africans.Despite many investigative studies on racial bias in face biometrics,very few studies on iris biometrics have been published,mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public domain.Fur-thermore,most of these databases contain a relatively small number of subjects and labelled images.This paper proposes a large-scale African database named Chinese Academy of Sciences Institute of Automation(CASIA)-Iris-Africa that can be used as a complement-ary database for the iris recognition community to mediate the effect of racial biases on Africans.The database contains 28 717 images of 1 023 African subjects(2046 iris classes)with age,gender,and ethnicity attributes that can be useful in demographically sensitive studies of Africans.Sets of specific application protocols are incorporated with the database to ensure the database's variability and scalability.Performance results of some open-source state-of-the-art(SOTA)algorithms on the database are presented,which will serve as baseline performances.The relatively poor performances of the baseline algorithms on the proposed database despite better performance on oth-er databases prove that racial biases exist in these iris recognition algorithms.The database will be made available on our website:http://www.idealtest.org.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing

    Dezheng WangYinglong WangFan YangLiyang Xu...
    400-410页
    查看更多>>摘要：In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure online.The data-driven soft sensor is an effective solution be-cause it provides a reliable and stable online estimation of such variables.This paper employs a deep neural network with multiscale fea-ture extraction layers to build soft sensors,which are applied to the benchmarked Tennessee-Eastman process(TEP)and a real wind farm case.The comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advant-ages over other methods.First,the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural networks.Second,the multiscale feature extraction layers can powerfully extract dataset characteristics.Finally,the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.
      原文链接:
    • NETL
    • NSTL
    • 万方数据