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Optik 2019年 180卷 847-857页

作者： Jing Dong Cheng Qiu Le Sun Jian Xin Zhang Christian Desrosiers Bin Liu Jian Lu Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian 116622 China School of Information and Communication Engineering Dalian University of Technology Dalian 116024 China Laboratory for Imagery Vision and Artificial Intelligence (LIVAI) École de technologie supérieure (ETS) Montreal H3C1K3 Canada International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China

Recently, deep hashing has achieved excellent performance in large-scale image retrieval by simultaneously learning deep features and hash function. However, state-of-the-art methods for this task have so far failed to explore feature statistics higher than first-order. To address this problem, we propose two novel deep high-order supervised hashing (DHoSH) architectures based on point-wise labels (DHoSH-PO) and pair-wise labels (DHoSH-PA), respectively. The core of DHoSH is a trainable layer of bilinear features incorporated into existing deep convolutional neural network (CNN) for end-to-end learning. This layer captures the interaction of local features by bilinear pooling, using correlation to model dependencies of features within the same layer or cross-correlation for features across different layers of the CNN. Furthermore, DHoSH systematically employs the high-order statistics of features of multiple layers. Extensive experiments on commonly used image retrieval benchmarks show that our DHoSH-PO and DHoSH-PA models yield improved accuracy over its first-order counterparts and achieve effective performance for these benchmarks.

关键词： High-order statistics Supervised hashing Bilinear pooling CNN Image retrieval

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Exploring risk factors and predicting UPDRS score based on Parkinson's speech signals 19

Exploring risk factors and predicting UPDRS score based on P...

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19th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2017

作者： Zhang, Jianxin Xu, Weifeng Zhang, Qiang Jin, Bo Wei, Xiaopeng Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China Dalian University of Technology School of Computer Science Dalian China

ISBN: (纸本)9781509067046

The unified Parkinson's disease rating scale (UPDRS) is the most widely employed scale for tracking Parkinson's disease (PD) symptom progression. However, conventional way to achieve UPDRS, mainly based on the physical examinations of clinic patients performed by the trained medical staffs, involves the disadvantages of inconvenience and high medical expense. Hence, in this study, we try to explore some risk factors and accurately predict the UPDRS for PD, using the speech signals of PD patients published on UCI machine-learning archive. More specifically, inspired by the idea of ensemble learning, we firstly construct a framework of ensemble feature selection (EFS) to select a suitable subset of features among numerous speech signals. Subsequently, a personalized predictive model, trained by adopting information from similar patients, is developed to be customized for an individual PD patient. Finally, we employ the personalized predictive model to predict UPDRS score combined with various classical regression algorithms. Compared to conventional models, our study has a potential to capture more relevant risk factors and produces more accurate UPDRS score for individual patient. Experimental results on real-world dataset from UCI machine-learning archive show that our personalized predictive model gets a promising performance. © 2017 IEEE.

关键词： Forecasting

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Pedestrian Detection Using Regional Proposal Network with Feature Fusion

Pedestrian Detection Using Regional Proposal Network with Fe...

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Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Xiaogang Lv Xiaotao Zhang Yinghua Jiang Jianxin Zhang Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian P. R. China Information and Engineering College Dalian University Dalian P. R. China

ISBN: (纸本)9781538664292;9781538664285

Pedestrian detection, which has broad application prospects in video security, robotics and self-driving vehicles etc., is one of the most important research fields in computer vision. Recently, deep learning methods, e.g., Region Proposal Network (RPN), have achieved major performance improvements in pedestrian detection. In order to further utilize the deep pedestrian features of RPN, this paper proposes a novel regional proposal network model based on feature fusion (RPN_FeaFus) for pedestrian detection. RPN_FeaFus adopts an asymmetric dual-path deep model, constructed by VGGNet and ZFNet, to extract pedestrian features in different levels, which are further combined through PCA dimension reduction and feature stacking to provide more discriminant representation. Then, the low-dimensional fusion features are adopted to detect the region proposals and train the classifier. Experimental results on three widely used pedestrian detection databases, i.e, Caltech database, Daimler database and TUD database, illuminate that RPN_FeaFus gains obvious performance improvements over its baseline RPN_BF, which is also competitive with the state-of-the-art methods.

关键词： Feature extraction Databases Proposals Task analysis Training Principal component analysis Detectors

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LSTM based classification model and its application for doctor-patient relationship evaluation 19

LSTM based classification model and its application for doct...

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19th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2017

作者： Kuang, Hongrui Che, Chao Zhang, Qiang Wei, Xiaopeng Key Lab of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9781509067046

The emergence of medical social media has made it possible for more and more patients to share their views and experiences on the medical care platform. These subjective texts contains patients' evaluation information for doctors and can be analyzed to provide rich decision-making information for patients and hospitals. Therefore, we propose a LSTM (Long Short Term Memory) based text sentiment classification method to evaluate the relationship between doctors and patients through the comment data from medical social media. The classification model of doctor-patient relationship can also be performed to evaluate the hospital by calculating the praise rate to help people choose hospitals. We perform two experiments on the patients' evaluation data from the website of Haodaifu. The experiment of doctor-patient relationship classification confirms the effectiveness of the classification model. In the experiment of hospital evaluation, we calculate the praise rate of 11 hospitals in Jinan of Shandong province based on the doctor-patient relationship classification results. The consistency between the results obtained by our method and the data of Mingyihui shows that our evaluation method for hospitals is reasonable and effective. © 2017 IEEE.

关键词： Hospitals

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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An Improved Correlation-Based Just-in-Time Modeling Method Using Dynamic Partial Least Squares and Adaptive Local Domain Partition 29

An Improved Correlation-Based Just-in-Time Modeling Method U...

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第29届中国控制与决策会议

作者： Xiaolong Chen Zhizhong Mao Runda Jia Dong Xiao Xiaojun Wang College of Information Science & Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Key Laboratory of Advanced Design and Intelligent Computing Dalian university

ISBN: (纸本)9781509046584

This paper proposes an improved correlation-based just-in-time modeling method,referring to as the ICo JIT,for improving the prediction accuracy and real-time performance of the conventional correlation-based just-in-time(CoJIT) modeling *** achieve this objective,a novel adaptive local domain partition method has been developed based on the moving window technique and the fitting precision,which takes into account the input and output information simultaneously and has potentially the capabilities of obtaining the optimal local domain partition adaptively and capturing new process states by adding new local *** dynamic partial least squares and adaptive local domain partition method,multiple local domains and corresponding local models can be obtained during the offline operation *** online computation burden is reduced compared to CoJIT modeling *** addition,the proposed ICoJIT modeling method can efficiently deal with nonlinearity and time-varying behavior of processes as well as the CoJIT modeling *** effectiveness of the proposed method is demonstrated through a real industrial process dataset in sulfur recovery unit process.

关键词： Soft sensor Correlation-based just-in-time modeling Partial least squares Local domain partition Moving window

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Palmprint recognition based on CNN and local coding features 6

Palmprint recognition based on CNN and local coding features

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6th International Conference on Computer Science and Network Technology, ICCSNT 2017

作者： Yang, Aoqi Zhang, Jianxin Sun, Qiule Zhang, Qiang Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China Faculty of Electronic Information and Electric Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781538604922

The convolutional neural network(CNN) has received an excellent performance in the recent tasks of image retrieval and image classification. Besides, some local coding methods have been focused on because of their outstanding local description. In this paper, we present a novel method for palmprint recognition, which combines CNN and local coding techniques. We conduct feature extraction on palmprint images using the pre-trained networks and obtain the local convolutional features. Then three different local coding methods are adopted to code the features from CNN, including Bag of Visual Word(BoVW), Locality-constrained Linear Coding(LLC) and Vector of Locally Aggregated Descriptors (VLAD). The features extracted from CNN have better representation. Meanwhile, the local coding makes local features more important. Hence the combination of them can get the anticipated better results. The proposed method is extensively evaluated on the PolyU palmprint database and PolyU multispectral palmprint database, illuminating the remarkable performance on palmprint recognition. © 2017 IEEE.

关键词： Palmprint recognition

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Allosteric DNAzyme-based DNA logic circuit: operations and dynamic analysis

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Nucleic acids research 2019年第3期47卷 1097-1109页

作者： Xuedong Zheng Jing Yang Changjun Zhou Cheng Zhang Qiang Zhang Xiaopeng Wei College of Computer Science Shenyang Aerospace University Shenyang 110136 China. School of Control and Computer Engineering North China Electric Power University Beijing 102206 China. College of Mathematics and Computer sciences Zhejiang Normal University Jinhua 321004 China. School of Electronics Engineering and Computer Science Peking University Key laboratory of High Confidence Software Technologies Ministry of Education Beijing 100871 China. Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China. School of Computer Scicence and Technology Dalian University of Technology Dalian 116024 China.

Recently, due to the dual roles of DNA and enzyme, DNAzyme has been widely used in the field of DNA circuit, which has a wide range of applications in bio-engineered system, information processing and biocomputing. In fact, the activity of DNAzymes was regulated by subunits assembly, pH control and metal ions triggers. However, those regulations required to change the sequences of whole DNAzyme, as separating parts and inserting extra DNA sequence. Inspired by the allosteric regulation of proteins in nature, a new allosteric strategy is proposed to regulate the activity of DNAzyme without DNA sequences changes. In this strategy, DNA strand displacement was used to regulate the DNAzyme structure, through which the activity of DNAzyme was well controlled. The strategy was applied to E6-type DNAzymes, and the operations of DNA logic circuit (YES, OR, AND, cascading and feedback) were established and simulated with the dynamic analyses. The allosteric regulation has potential to construct more complicated molecular systems, which can be applied to bio-sensing and detection.

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The soft sensor of the molten steel temperature using the modified maximum entropy based pruned bootstrap feature subsets ensemble method

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Chemical Engineering Science 2018年 189卷 401-412页

作者： Xiao-Jun Wang Xiao-Yan Wang Qiang Zhang Zhi-Zhong Mao Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian 116622 China School of Business Administration Dongbei University of Finance and Economics Dalian 116025 China Institute of Automatization College of Information Science and Engineering Northeastern University Shenyang 110004 China

The molten steel temperature in ladle furnace is a significant variable, but it is hard to be measured by real-time detection, which has some bad effects on productions. Soft sensors are alternative and effective techniques to solve this issue. In this paper, the soft sensor of the molten steel temperature established by the Modified Maximum Entropy based Pruned Bootstrap Feature Subsets Ensemble (MMEP-BFSE) method is proposed. Although the Bootstrap Feature Subsets Ensemble (BFSE) temperature model is prominent in the precision and the forecasting speed on the large-scale and noisy data, its main drawback is too many sub-models required to combine, which is not always feasible for applications. To alleviate this drawback, the Modified Maximum Entropy based Pruning (MMEP) approach is presented, in which a subset of sub-models that better approximates the complete ensemble is find based on the maximum Rényi entropy and the trade-off parameter between the precision and the diversity of sub-models. Then, the soft sensor of the temperature based on the MMEP-BFSE is established on the practical data. Experiments show that the proposed soft sensor outperforms the others in the precision, and meets the precision requirements. Sub-models of the BFSE temperature model are substantially pruned with improved generalization by the MMEP approach.

关键词： Chemical process Quality variables Soft sensor Data-driven model Machine learning Ensemble pruning

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design and Implementation of Ideological and Political Education Evaluation System

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Journal of Physics: Conference Series 2019年第4期1176卷

作者： Zhenyuan Lv Changjun Zhou Chunye Zhou Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Ministry of Education Dalian 116622 China School of Politics and Law Shangrao Normal University ShangraoChina 334001 China

With the development of informatization, in order to improve office efficiency and manage students more effectively, universities have produced many management systems, such as library management systems and educational management systems. These systems mostly manage student information and other information in an integrated manner. The refinement of each module, due to the large number of colleges and universities, graduates and enrolls a large number of students every year, and the archiving of students' ideological and political courses and students' ideological and political evaluation is quite complicated. Some college management systems may only provide ideological and political courses. Course information and student test scores did not specifically manage the student's ideological and political education assessment and assess the student's psychological activities based on the student's daily performance, nor did they provide detailed course scheduling information for specific ideological and political related courses. Therefore, colleges and universities should also implement detailed information management for students' ideological and political education methods, which can improve management efficiency and reduce unnecessary human resources. The management system is developed by the Java[1-3]development language through Myeclipise as a development tool. The mysql database is used as the background to realize the information management of the ideological and political education of college students. It can promote the detailed management of all aspects of students' information management. Service school as well as students. And the machine learning algorithm is added. Through the previous students' data, the classification model is trained by Logistic regression algorithm to evaluate the students' ideological and political good condition.

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