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Palmprint recognition using phase congruency and region-based orientation code

Journal of Computational and Theoretical Nanoscience 2016年第4期13卷 2461-2466页

作者： Zhang, Jianxin Liu, Ligang Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116622 China

In this paper, a novel palmprint recognition method by a combination of phase congruency model and region-based orientation code approach is proposed. Palmprint phase congruency feature images are firstly achieved by the multi-scale and multi-orientation 2D Log-Gabor wavelet filters. Then, a region orientation code approach, which is based on the orientation competition result of the sub-region images, is put forward to give more compact representation of palmprint phase congruency feature images. The proposed palmprint recognition method can provide more robust discriminant feature. The experiment results on the public PolyU palmprint database show the good recognition performance of proposed method. © Copyright 2016 American Scientific Publishers All rights reserved.

关键词： Palmprint recognition

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多组学基因大数据与精准医学

多组学基因大数据与精准医学

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浙江省生物信息学学会第五届学术年会暨大数据时代精准医学国际学术研讨会

作者： Yi Zhao Bioinformatics Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research LaboratoryInstitute of Computing TechnologyChineseAcademy of Sciences LuoYang Branch of Institute of Computing Technology Chinese Academy of Sciences

伴随着高通量测序技术等生物技术的发展,基因组、转录组、表观组、蛋白组、代谢组、微生物组、暴露组等多组学测序数据可以在几天之内迅速产出,并被广泛应用于精准医学的研究和临床应用,这些庞大而复杂的数据不但构成了精确医学所需要... 详细信息

伴随着高通量测序技术等生物技术的发展,基因组、转录组、表观组、蛋白组、代谢组、微生物组、暴露组等多组学测序数据可以在几天之内迅速产出,并被广泛应用于精准医学的研究和临床应用,这些庞大而复杂的数据不但构成了精确医学所需要的关键信息,而且也是该领域内增长最快的数据类型。当前,精准医学计划在多个国家陆续启动,各国科学家们正致力于收集十万、百万人群的基因组信息用来构建权威、且全新的人口健康大数据知识体系。在这样的时代背景下,作为人口大国,中国精准医学迫切的需要建立属于中国人自己的基因数据库。2016年,中国科技部将"精准医学研究"列为重大研究计划,中科院也于2016年启动中国人群精准医学研究计划,4年内预计完成4000志愿者的DNA样本和多种表现型数据的采集。对此,我们可以预见,随着大队列研究的开展,以基因组为代表的多组学大数据仍将会不断产生,但是这些组学大数据本身并不能产生价值,其价值在于与疾病表型等其他信息进行关联分析、挖掘并转换为知识,才能进一步被应用于医疗及健康决策。因此,促进大数据到知识的转化,构建基因大数据研究与应用的一体化平台体系,才是精准医学到临床的关键一步,其将会被应用于疾病的早期筛查、分子分型、个体化治疗、疗效预测及监控等多个临床决策场景。多组学精准医学大数据的发展和研究也面临着一些挑战,例如:数据共享与安全隐私问题、多维度组学数据的整合分析问题、生物信息数据分析方法的标准化以及解读规范化等等问题。因此,需要更多国家政策的支持、需要IT及人工智能等领域的学者贡献力量、需要各个领域的专家来制定行业标准,最终推动精准医疗向自动化,智能化,标准化,简单化方向发展。

关键词：精准医学多组学大数据

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Tree-Structure Ensemble General Regression Neural Networks applied to predict the molten steel temperature in Ladle Furnace

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advanced Engineering Informatics 2016年第3期30卷 368-375页

作者： Xiaojun Wang Mingshuang You Zhizhong Mao Ping Yuan Key Laboratory of Advanced Design and Intelligent Computing (Dalian university) Ministry of Education Dalian 116622 China Institute of Automatization Northeastern University Shenyang 110004 China

To control the molten steel temperature in a Ladle Furnace accurately, it is necessary to build a precise (i.e. accurate and good generalized) temperature prediction model. To solve this problem, ensemble modeling methods have been applied to predict the temperature. Now, in the production process, large-scale data with more helpful information are sampled, which provides possibilities to improve the precision of the temperature prediction. Although most of the existing ensemble temperature models have strong learning ability, they are not suitable for the large-scale data. In this paper, to solve the large-scale issue, the Tree-Structure Ensemble General Regression Neural Networks (TSE-GRNNs) method is proposed. Firstly, small-scale sample subsets are constructed based on the regression tree algorithm. Secondly, GRNN sub-models are built on sample subsets, which can be designed very quickly and cannot converge to poor solutions according to local minima of the error criterion. Then, the TSE-GRNNs method is applied to establish a temperature model. Experiments show that the TSE-GRNNs temperature model is more precise than the other existing temperature models, and meets the requirements of the RMSE and the maximum error of the molten steel temperature prediction in Ladle Furnace.

关键词： Ladle Furnace Temperature prediction model Ensemble method General Regression Neural Networks

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Molten steel temperature prediction model based on bootstrap Feature Subsets Ensemble Regression Trees

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Knowledge-Based Systems 2016年 101卷 48-59页

作者： Xiaojun Wang Ping Yuan Zhizhong Mao Mingshuang You Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Ministry of Education Dalian 116622 China Institute of Automatization Northeastern University Shenyang 110004 China

Molten steel temperature prediction is important in Ladle Furnace (LF). Most of the existing temperature models have been built on small-scale data. The accuracy and the generalization of these models cannot satisfy industrial production. Now, the large-scale data with more useful information are accumulated from the production process. However, the data are with noise. Large-scale and noise data impose strong restrictions on building a temperature model. To solve these two issues, the Bootstrap Feature Subsets Ensemble Regression Trees (BFSE-RTs) method is proposed in this paper. Firstly, low-dimensional feature subsets are constructed based on the multivariate fuzzy Taylor theorem, which saves more memory space in computers and indicates ``smaller-scale'' data sets are used. Secondly, to eliminate the noise, the bootstrap sampling approach of the independent identically distributed data is applied to the feature subsets. Bootstrap replications consist of smaller-scale and lower-dimensional samples. Thirdly, considering its simplicity, a Regression Tree (RT) is built on each bootstrap replication. Lastly, the BFSE-RTs method is used to establish a temperature model by analyzing the metallurgic process of LF. Experiments demonstrate that the BFSE-RTs outperforms other estimators, improves the accuracy and the generalization, and meets the requirements of the RMSE and the maximum error on the temperature prediction.

关键词： Ladle furnace Molten steel temperature prediction Large-scale data and noise data Ensemble method

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Precessing jet nozzle connecting to a spinning black hole in M87

arXiv

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arXiv 2023年

作者： Cui, Yuzhu Hada, Kazuhiro Kawashima, Tomohisa Kino, Motoki Lin, Weikang Mizuno, Yosuke Ro, Hyunwook Honma, Mareki Yi, Kunwoo Yu, Jintao Park, Jongho Jiang, Wu Shen, Zhiqiang Kravchenko, Evgeniya Algaba, Juan-Carlos Cheng, Xiaopeng Cho, Ilje Giovannini, Gabriele Giroletti, Marcello Jung, Taehyun Lu, Ru-Sen Niinuma, Kotaro Oh, Junghwan Ohsuga, Ken Sawada-Satoh, Satoko Sohn, Bong Won Takahashi, Hiroyuki R. Takamura, Mieko Tazaki, Fumie Trippe, Sascha Wajima, Kiyoaki Akiyama, Kazunori An, Tao Asada, Keiichi Buttaccio, Salvatore Byun, Do-Young Cui, Lang Hagiwara, Yoshiaki Hirota, Tomoya Hodgson, Jeffrey Kawaguchi, Noriyuki Kim, Jae-Young Lee, Sang-Sung Lee, Jee Won Lee, Jeong Ae Maccaferri, Giuseppe Melis, Andrea Melnikov, Alexey Migoni, Carlo Oh, Se-Jin Sugiyama, Koichiro Wang, Xuezheng Zhang, Yingkang Chen, Zhong Hwang, Ju-Yeon Jung, Dong-Kyu Kim, Hyo-Ryoung Kim, Jeong-Sook Kobayashi, Hideyuki Li, Bin Li, Guanghui Li, Xiaofei Liu, Zhiyong Liu, Qinghui Liu, Xiang Oh, Chung-Sik Oyama, Tomoaki Roh, Duk-Gyoo Wang, Jinqing Wang, Na Wang, Shiqiang Xia, Bo Yan, Hao Yeom, Jae-Hwan Yonekura, Yoshinori Yuan, Jianping Zhang, Hua Zhao, Rongbing Zhong, Weiye Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Tsung-Dao Lee Institute Shanghai Jiao Tong University 520 Shengrong Road Shanghai201210 China Astronomical Science Program The Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Mizusawa VLBI Observatory National Astronomical Observatory of Japan 2-12 Hoshigaoka Mizusawa Iwate Oshu023-0861 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Kogakuin University of Technology & Engineering Academic Support Center 2665-1 Nakano-machi Hachioji Tokyo192-0015 Japan South-Western Institute For Astronomy Research Yunnan University Kunming650500 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Straße 1 Frankfurt am MainD-60438 Germany Korea Astronomy & Space Science Institute Daedeokdae-ro 776 Yuseong-gu Daejeon34055 Korea Republic of Department of Astronomy Yonsei University Yonsei-ro 50 Seodaemun-gu Seoul03722 Korea Republic of Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Department of Physics and Astronomy Seoul National University Gwanak-gu Seoul08826 Korea Republic of Department of Information Countermeasure Air Force Early Warning Academy Wuhan430019 China Institute of Astronomy and Astrophysics Academia Sinica HiloHI96720 United States Shanghai Astronomical Observatory Chinese Academy of Sciences 80 Nandan Road Shanghai200030 China Key Laboratory of Radio Astronomy Chinese Academy of Sciences Beijing100101 China Moscow Institute of Physics and Technology Dolgoprudny Institutsky per. 9 Moscow141700 Russia Lebedev Physical Institute The Russian Academy of Sciences Leninsky prospekt 53 Moscow119991 Russia Department of Physics Faculty of Science U

The nearby radio galaxy M87 offers a unique opportunity to explore the connections between the central supermassive black hole and relativistic jets. Previous studies of the inner region of M87 revealed a wide opening angle for the jet originating near the black hole 1–4. The Event Horizon Telescope resolved the central radio source and found an asymmetric ring structure consistent with expectations from General Relativity 5. With a baseline of 17 years of observations, there was a shift in the jet’s transverse position, possibly arising from an eight to ten-year quasi-periodicity 3. However, the origin of this sideways shift remains unclear. Here we report an analysis of radio observations over 22 years that suggests a period of about 11 years in the position angle variation of the jet. We infer that we are seeing a spinning black hole that induces the Lense-Thirring precession of a misaligned accretion disk. Similar jet precession may commonly occur in other active galactic nuclei but has been challenging to detect owing to the small magnitude and long period of the variation. Copyright © 2023, The Authors. All rights reserved.

关键词： Active Galactic Nuclei

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Neural network for heterogeneous annotations

Neural network for heterogeneous annotations

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2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016

作者： Chen, Hongshen Zhang, Yue Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Singapore University of Technology and Design Singapore ADAPT Centre School of Computing Dublin City University Ireland

ISBN: (纸本)9781945626258

Multiple treebanks annotated under heterogeneous standards give rise to the research question of best utilizing multiple resources for improving statistical models. Prior research has focused on discrete models, leveraging stacking and multi-view learning to address the problem. In this paper, we empirically investigate heterogeneous annotations using neural network models, building a neural network counterpart to discrete stacking and multi-view learning, respectively, finding that neural models have their unique advantages thanks to the freedom from manual feature engineering. Neural model achieves not only better accuracy improvements, but also an order of magnitude faster speed compared to its discrete baseline, adding little time cost compared to a neural model trained on a single treebank. © 2016 Association for Computational Linguistics

关键词： Forestry

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A micro-genetic algorithm for DNA encoding sequences design

A micro-genetic algorithm for DNA encoding sequences design

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IEEE International Conference on Control Science and Systems Engineering (CCSSE)

作者： Xinmei Peng Xuedong Zheng Bin Wang Changjun Zhou Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian P. R. China

ISBN: (纸本)9781467398732

Aiming at DNA encoding sequences design, a micro-genetic algorithm (MGA) is proposed by introducing a sharing function based on similarity and H-measure of DNA sequences. In the algorithm, six design criteria are adopted and four genetic operators are applied. Compared with the previous results, the algorithm can get better DNA sequences and improve the computational efficiency.

关键词： DNA Encoding Algorithm design and analysis Sociology Statistics DNA computing

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A throughput aware with collision-free MAC for wireless LANs

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Science China(Information Sciences) 2016年第2期59卷 240-242页

作者： Tingrui PEI Yafeng DENG Zhetao LI Gengming ZHU Gaofeng PAN Youngjune CHOI Hiroo SEKIYA College of Information Engineering Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Computer Science & Engineering Hunan University of Science and Technology School of Electronic and Information Engineering Southwest University Department of Information and Computer Engineering Ajou University Graduate School of Advanced Integration Science Chiba University

Dear editor,The Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA)hybrid with the resource reservation approach from Time Division Multiple Address(TDMA)has been emerged as a promising method to solve collision problems in wireless LANs[1–6].In the hybrid method,a TDMA circle contains multiple slots for nodes to contend for the ***,it allows a

关键词： MAC CSCF free TDMA A throughput aware with collision-free MAC for wireless LANs

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Finite Element Analysis on Loosen Teeth using Fibrous Periodontal Splint Restoration

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IOP Conference Series: Materials Science and Engineering 2018年第1期417卷

作者： Chengshui Ding Xue Shi Yang Song Lulu Qiu Xinyue Li Chunqiu Zhang Tianjin Key Laboratory of the Design Intelligent Control of the Advanced Mechatronical System National Demonstration Center for Experimental Mechanical and Electrical Engineering Education School of Mechanical Engineering Tianjin University of Technology Tianjin 300384 China Periodontitis Department Tianjin Stomatological Hospital Tianjin 300384 China

Objective To investigate the effect of fibre periodontal splint restoration on mandibular anterior teeth displacement and periodontal membrane stress. Methods Micro-CT scanning technology, combined with Mimics, Geomagic studio and SolidWorks were used to establish 3D dental models of mandibles with teeth and the models fibre periodontal splint restoration; the models of mandibular anterior loosen teeth was constructed under 30 degree oblique 25N loading on the labial and lingual sides respectively. Then, under different angles force with or without the splint, the analysis on dental mechanical state are given. Results It was determined that the sum of the maximal displacement of the loosen tooth model at the lingual and labial sides was 1 mm, which represented the degree I loosen model. After splint repair on the model, the maximal displacement of loosen tooth decreased 47.7%, 85.1% and 85.8% at 0 degree, 15 degree and 30 degree forces respectively, and the maximum stress of periodontal membrane decreased by 22.3%, 61.2% and 74.9%. Conclusion Fixation of loosen teeth through fibre periodontal splints can reduce the maximum stress of periodontal membrane and the maximal displacement of the teeth, thus ensuring the stability of loosen tooth.

关键词：

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Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs

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BMC bioinformatics 2018年第SUPPL 19期19卷 521页

作者： Yuanlin Ma Zuguo Yu Guosheng Han Jinyan Li Vo Anh Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. yuzuguo@***. School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia. yuzuguo@***. Advanced Analytics Institute Faculty of Engineering & IT University of Technology Sydney P.O Box 123 Broadway NSW 2007 Australia. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia.

BACKGROUND:Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. RESULTS:We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. CONCLUSIONS:The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs iden

关键词： Hibert-Huang transform Network PSI-BLAST profiles Pre-microRNA SVM mRMR

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