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Visual synchronization of two 3-variable Lotka-Volterra oscillators based on DNA strand displacement

RSC advances 2018年第37期8卷 20941-20951页

作者： Chengye Zou Xiaopeng Wei Qiang Zhang School of Computer Science and Engineering Dalian University of Technology Dalian 116024 China zhangq@***. Key Laboratory of Advanced and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China.

DNA strand displacement as a theoretic foundation is helpful in constructing reaction networks and DNA circuits. Research on chemical kinetics is significant to exploit the inherent potential property of biomolecular systems. In this study, we investigated two typical reactions and designed DNA strands with a fluorophore and dark quencher for reaction networks using a 3-variable Lotka-Volterra oscillator system as an example to show the convenience of and superiority for observation of dynamic trajectory using our design, and took advantage of the synchronization reaction module to synchronize two 3-variable Lotka-Volterra oscillators. The classical theory of chemical reaction networks can be used to represent biological processes for mathematical modeling. We used this method to simulate the nonlinear kinetics of a 3-variable Lotka-Volterra oscillator system.

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Reversible data hiding in encrypted images with Two-MSB prediction 10

Reversible data hiding in encrypted images with Two-MSB pred...

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10th IEEE International Workshop on Information Forensics and Security, WIFS 2018

作者： Puyang, Yi Yin, Zhaoxia Qian, Zhenxing Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University Hefei230601 China Department of Computer Science Purdue University West lafayette47906 United States Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200072 China

ISBN: (纸本)9781538665367

In recent years, reversible data hiding in encrypted images (RDHEI) that embeds additional data into the encrypted image content has received more and more attention. In previous RDHEI methods, there is no one considering the correlation between neighboring pixels. In this paper, to better explore the correlation between neighbor pixels, a novel reversible data hiding method in encrypted images with two-MSB (MSB and second MSB) prediction is proposed. After analyzing the original image content and detecting all the possible prediction errors, we encrypt the original image content using the bitwise exclusive-or (XOR) operation and highlight prediction error based on the error location binary map. Finally, all available pixels are embedded with additional data by two-MSB substitution. In our method, reversibility, separability and error-free data extraction are realized. The experimental results show that the proposed method providing a higher embedding rate and better quality of directly decrypted image compared with other start-of-the-art methods. © 2018 IEEE.

关键词： Pixels

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Deep high-order supervised hashing

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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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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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Logic operation model of the complementer based on two-domain DNA strand displacement 1

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12th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2017

作者： Xie, Wendan Zhou, Changjun Lv, Hui Zhang, Qiang Key Laboratory of Advanced Design Intelligent Computing Ministry of Education Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9789811071799

ISBN: (纸本)9789811071782

DNA strand replacement technology has the advantages of simple operation which makes it becomes a common method of DNA computing. A four bit binary number Complementer based on two-domain DNA strand displacement is proposed in this paper. It implements the function of converting binary code into complement code. Simulation experiment based on Visual DSD software is carried out. The simulation results show the correctness and feasibility of the logic model of the Complementer, and it makes useful exploration for further expanding the application of molecular logic circuit. © Springer Nature Singapore Pte Ltd 2017.

关键词： Timing circuits

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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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An Image Encryption Algorithm Based on Chaos System and DNA Strand Displacement Model

An Image Encryption Algorithm Based on Chaos System and DNA ...

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2017 2nd International Conference on Artificial Intelligence: Techniques and Applications (AITA 2017)

作者： Ze-jun ZHANG Bo-xiang XIAO Xue-dong ZHENG Chang-jun ZHOU Key Laboratory of Advanced Design and Intelligent Computing(Dalian University) Ministry of Education

In this paper,we proposed a novel algorithm for image *** the scheme,three chaotic sequences are generated by Lorenz chaos *** chaotic sequences are used to permutation and diffusion the digital ***,the algorithm introduce the DNA strand displacement reaction model to extract the secret key and then encrypt the image with a combination of XOR *** simulation results and security analysis show that our algorithm can successfully encrypts and decrypts the input image and gets a better encryption ***,the method also has a good ability of resisting attacks.

关键词： Image encryption Chaos system DNA strand displacement XOR operation

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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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