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Optimal control approach to stability criteria on Hill's equations

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Applied Mathematics(A Journal of Chinese Universities) 2018年第3期33卷 253-273页

作者： ZU Jian LI Yong School of Mathematics and Statistics & Center for Mathematics and Interdisciplinary Sciences North-east Normal University key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University College of Mathematics Jilin University

This paper gives a tutorial on how to prove Lyapunov type criteria by optimal control methods. Firstly, we consider stability criteria on Hill’s equations with nonnegative potential. By optimal control methods developed in 1990s, we obtain several stability criteria including Lyapunov’s criterion, Neǐgauz and Lidskiǐ’s criterion. Secondly, we present stability criteria on Hill’s equations with sign-changing potential in which Brog’s criterion and Krein’s criterion are included.

关键词： stability criteria optimal control method Hill's equation Lyapunov inequality

Research on Trust Prediction from a Sociological Perspective

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Journal of Computer Science & Technology 2015年第4期30卷 843-858页

作者：王英王鑫左万利 College of Computer Science and Technology Jilin University Changchun 130012 China key laboratory of symbolic computation and knowledge engineering Ministry of Education Changchun 130012 China School of Computer Technology and engineering Changehun Institute of Technology Changchun 130012 China

Trust, as a major part of human interactions, plays an important role in helping users collect reliable infor-mation and make decisions. However, in reality, user-specified trust relations are often very sparse and follow a power law distribution; hence inferring unknown trust relations attracts increasing attention in recent years. Social theories are frameworks of empirical evidence used to study and interpret social phenomena from a sociological perspective, while social networks reflect the correlations of users in real world; hence, making the principle, rules, ideas and methods of social theories into the analysis of social networks brings new opportunities for trust prediction. In this paper, we investigate how to exploit homophily and social status in trust prediction by modeling social theories. We first give several methods to compute homophily coe？cient and status coe？cient, then provide a principled way to model trust prediction mathe-matically, and propose a novel framework, hsTrust, which incorporates homophily theory and status theory. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of homophily theory and status theory in trust prediction.

关键词： trust prediction homophily coefficient status coefficient social theory matrix factorization

KAM Type-Theorem for Lower Dimensional Tori in Random Hamiltonian Systems

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Communications in Mathematical Research 2011年第1期27卷 81-96页

作者： LI YONG XU LU College of Mathematics Key Laboratory of Symbolic computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012

In this paper, we study the persistence of lower dimensional tori for random Hamiltonian systems, which shows that majority of the unperturbed tori persist as Cantor fragments of lower dimensional ones under small per... 详细信息

关键词： random Hamiltonian system KAM type theorem Cantor fragment of invariant tori

Experience Replay for Least-Squares Policy Iteration

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IEEE/CAA Journal of Automatica Sinica 2014年第3期1卷 274-281页

作者： Quan Liu Xin Zhou Fei Zhu Qiming Fu Yuchen Fu the School of Computer Science and Technology Soochow University the key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University the School of Computer Science and Technology Soochow University

Policy iteration,which evaluates and improves the control policy iteratively,is a reinforcement learning *** evaluation with the least-squares method can draw more useful information from the empirical data and therefore improve the data ***,most existing online least-squares policy iteration methods only use each sample just once,resulting in the low utilization *** the goal of improving the utilization efficiency,we propose an experience replay for least-squares policy iteration(ERLSPI)and prove its *** method combines online least-squares policy iteration method with experience replay,stores the samples which are generated online,and reuses these samples with least-squares method to update the control *** apply the ERLSPI method for the inverted pendulum system,a typical benchmark *** experimental results show that the method can effectively take advantage of the previous experience and knowledge,improve the empirical utilization efficiency,and accelerate the convergence speed.

关键词： Learning (artificial intelligence) Markov processes computational efficiency Convergence Least squares methods

DeepHBSP:A Deep Learning Framework for Predicting Human Blood-Secretory Proteins Using Transfer Learning

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Journal of Computer Science & Technology 2021年第2期36卷 234-247页

作者： Wei Du Yu Sun Hui-Min Bao Liang Chen Ying Li Yan-Chun Liang key laboratory of Symbol computation and knowledge engineering of ministry of education College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Computer Science College of EngineeringShantou UniversityShantou 515063China Zhuhai laboratory of key laboratory of Symbol computation and knowledge engineering of ministry of education Zhuhai College of Jilin University Zhuhai 519041China

The identification of blood-secretory proteins and the detection of protein biomarkers in the blood have an important clinical application *** methods for predicting blood-secretory proteins are mainly based on traditional machine learning algorithms,and heavily rely on annotated protein *** traditional machine learning algorithms,deep learning algorithms can automatically learn better feature representations from raw data,and are expected to be more promising to predict blood-secretory *** present a novel deep learning model(DeepHBSP)combined with transfer learning by integrating a binary classification network and a ranking network to identify blood-secretory proteins from the amino acid sequence information *** loss function of DeepHBSP in the training step is designed to apply descriptive loss and compactness loss to the binary classification network and the ranking network,*** feature extraction subnetwork of DeepHBSP is composed of a multi-lane capsule ***,transfer learning is used to train a highly accurate generalized model with small samples of blood-secretory *** main contributions of this study are as follows:1)a novel deep learning architecture by integrating a binary classification network and a ranking network is proposed,superior to existing traditional machine learning algorithms and other state-of-the-art deep learning architectures for biological sequence analysis;2)the proposed model for blood-secretory protein prediction uses only amino acid sequences,overcoming the heavy dependence of existing methods on annotated protein features;3)the blood-secretory proteins predicted by our model are statistically significant compared with existing blood-based biomarkers of cancer.

关键词： blood-secretory protein deep learning capsule network transfer learning

L-priorities Bloom Filter: A New Member of the Bloom Filter Family

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International Journal of Automation and computing 2012年第2期9卷 171-176页

作者： Huang-Shui Hu Hong-Wei Zhao Fei Mi College of Computer Science and Technology Jilin University Changchun 130012 PRC key laboratory of symbolic computation and knowledge engineering for ministry of education Jilin University Changchun 130012 PRC

A Bloom filter is a space-efficient data structure used for concisely representing a set as well as membership queries at the expense of introducing false positive. In this paper, we propose the L-priorities Bloom filter （LPBF） as a new member of the Bloom filter （BF） family, it uses a limited multidimensional bit space matrix to replace the bit vector of standard bloom filters in order to support different priorities for the elements of a set. We demonstrate the time and space complexity, especially the false positive rate of LPBF. Furthermore, we also present a detailed practical evaluation of the false positive rate achieved by LPBF. The results show that LPBF performs better than standard BFs with respect to false positive rate.

关键词： Bloom filter bit space matrix false positive L-priorities time and space complexity.

Curve length estimation based on cubic spline interpolation in gray-scale images

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Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第10期14卷 777-784页

作者： Zhen-xin WANG Ji-hong OUYANG College of Computer Science and Technology Jilin University MOE key laboratory of symbolic computation and knowledge engineering Jilin University

This paper deals with a novel local arc length estimator for curves in gray-scale *** method first estimates a cubic spline curve fit for the boundary points using the gray-level information of the nearby pixels,and then computes the sum of the spline segments’*** this model,the second derivatives and y coordinates at the knots are required in the computation;the spline polynomial coefficients need not be computed *** provide the algorithm pseudo code for estimation and preprocessing,both taking linear *** shows that the proposed model gains a smaller relative error than other state-of-the-art methods.

关键词： Arc length estimation Cubic spline interpolation Gray-scale image Local algorithm

Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

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Chinese Journal of Electronics 2020年第6期29卷 1153-1164页

作者： XU Qianyi QIN Guihe SUN Minghui YAN Jie JIANG Huiming ZHANG Zhonghan College of Computer Science and Technology Jilin University key laboratory of symbolic Computing and knowledge engineering ministry of education Jilin University College of Software Jilin University

Hand gesture recognition on the depth videos is a promising approach for automotive interfaces because it is less sensitive to light variation and more accurate than other traditional ***,video gestures recognition is still a challenging task since lots of interferences are induced by the uncorrelated gesture *** that if the displays are more relevant,the results will more accurate,so ResNext,a kind of compact and efficient neural network,is firstly used as feature extractor,then an improved weighted frame unification method is adopted to obtain the key frame samples,finally the Discriminant correlation analysis(DCA) is employed to fuse features for static data and dynamic data after conducting Feature embedding branch(FEB) on static *** public dataset named Depth based gesture recognition database(DGRD) is used in this paper,but the dataset is a little small and the class distribution is largely imbalance,and we find the performance of ResNext degrades badly in the condition of imbalance problem although it achieves excellent result at sufficient training *** order to conquer the disadvantages of limited dataset,a special loss function scheme combining the soft max loss and dice loss is *** of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed method is more practical for gesture recognition and may be widely adopted by automotive interfaces.

关键词： feature extraction gesture recognition learning (artificial intelligence) neural nets Depth based gesture recognition database Feature embedding branch dynamic data static data key frame samples improved weighted frame unification method feature extractor efficient neural network compact network uncorrelated gesture factors video gestures recognition depth videos automotive interfaces Feature fusion based hand gesture recognition method Weighted frame unification Discriminant correlation analysis (DCA) Feature embedding branch (FEB) Gesture recognition Loss function

A Cascaded Co-evolutionary Model for Attribute Reduction and Classification Based on Coordinating Architecture with Bidirectional Elitist Optimization

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Chinese Journal of Electronics 2017年第1期26卷 13-21页

作者： DING Weiping WANG Jiandong LI Yuehua CHENG Xueyun School of Computer Science and Technology Nantong University key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Provincial key laboratory for Computer Information Processing Technology Soochow University College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

A cascaded co-evolutionary model for Attribute reduction and classification based on Coordinating architecture with bidirectional elitist optimization(ARC-CABEO) is proposed for the more practical applications. The regrouping and merging coordinating strategy of ordinary-elitist-role-based population is introduced to represent a more holistic cooperative co-evolutionary framework of different populations for attribute reduction. The master-slave-elitist-based subpopulations are constructed to coordinate the behaviors of different elitists, and meanwhile the elitist optimization vector with the strongest balancing between exploration and exploitation is selected out to expedite the bidirectional attribute co-evolutionary reduction process. In addition, two coupled coordinating architectures and the elitist optimization vector are tightly cascaded to perform the co-evolutionary classification of reduction subsets. Hence the preferring classification optimization goal can be achieved better. Some experimental results verify that the proposed ARC-CABEO model has the better feasibility and more superior classification accuracy on different UCI datasets, compared with representative algorithms.

关键词： Attribute reduction for classification Bidirectional elitist optimization Coordinating architecture Cascaded co-evolutionary model