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Improved CS Algorithm and its Application in Parking Space Prediction

Journal of Bionic engineering 2020年第5期17卷 1075-1083页

作者： Rui Guo Xuanjing Shen Hui Kang College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China

This paper simulates the cuckoo incubation process and flight path to optimize the Wavelet Neural Network(WNN)model,and proposes a parking prediction algorithm based on WNN and improved Cuckoo Search(CS)***,the initialization parameters are provided to optimize the WNN using the improved *** traditional CS algorithm adopts the strategy of overall update and evaluation,but does not consider its own information,so the convergence speed is very *** proposed algorithm employs the evaluation strategy of group update,which not only retains the advantage of fast convergence of the dimension-by-dimension update evaluation strategy,but also increases the mutual relationship between the nests and reduces the overall running ***,we use the WNN model to predict parking *** proposed algorithm is compared with six different heuristic algorithms in five *** experimental results show that the proposed algorithm is superior to other algorithms in terms of running time and accuracy.

关键词： wavelet neural network cuckoo search algorithm available parking spaces prediction bionic

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An Algorithm for the Discretization of an Ideal Projector

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Journal of Systems Science & Complexity 2016年第5期29卷 1400-1410页

作者： JIANG Xue ZHANG Shugong LI Zhe Institute of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering (Ministry of Education) Jilin University Department of Applied Mathematics School of Science Changchun University of Science and Technology

Ideal interpolation is a generalization of the univariate Hermite interpolation. It is well known that every univariate Hermite interpolant is a pointwise limit of some Lagrange ***, a counterexample provided by Shekhtman Boris shows that, for more than two variables,there exist ideal interpolants that are not the limit of any Lagrange interpolants. So it is natural to consider: Given an ideal interpolant, how to find a sequence of Lagrange interpolants(if any) that converge to it. The authors call this problem the discretization for ideal interpolation. This paper presents an algorithm to solve the discretization problem. If the algorithm returns "True", the authors get a set of pairwise distinct points such that the corresponding Lagrange interpolants converge to the given ideal interpolant.

关键词： Discretization ideal interpolation Lagrange interpolation.

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Simultaneous Prediction for Multiple key Performance Indicators in Semiconductor Wafer Fabrication

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Chinese Journal of Electronics 2016年第6期25卷 1159-1165页

作者： CAO Zhengcai LIU Xuelian HAO Jinghua LIU Min College of Information Science and Technology Beijing University of Chemical Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Department of Automation Tsinghua University

The prediction and key factors identification for lot Cycle time(CT) and Equipment utilization(EU) which remain the key performance indicators(KPI)are vital for multi-objective optimization in semiconductor manufacturing industry. This paper proposes a prediction methodology which predicts CT and EU simultaneously and identifies their key factors. Bayesian neural network(BNN) is used to establish the simultaneous prediction model for Multiple key performance indicators(MKPI),and Bayes theorem is key solution in model complexity controlling. The closed-loop structure is built to keep the stability of MKPI prediction model and the weight analysis method is the basis of identifying the key factors for CT and EU. Compared with Artificial neural network(ANN)and Selective naive Bayesian classifier(SNBC), the simulation results of the prediction method of BNN are proved to be more feasible and effective. The prediction accuracy of BNN has been obviously improved than ANN and SNBC.

关键词： Prediction for MKPI Cycle time Equipment utilization BNN key factors identification

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An improved fruit fly optimization algorithm for solving traveling salesman problem

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Frontiers of Information Technology & Electronic engineering 2017年第10期18卷 1525-1533页

作者： Lan HUANG Gui-chao WANG Tian BAI Zhe WANG College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University) Ministry of Education Changchun 130012 China

The traveling salesman problem(TSP), a typical non-deterministic polynomial(NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimization algorithm(FOA) is used to solve TSP, since it has the advantages of being easy to understand and having a simple implementation. However, it has problems, including a slow convergence rate for the algorithm, easily falling into the local optimum, and an insufficient optimization precision. To address TSP effectively, three improvements are proposed in this paper to improve FOA. First, the vision search process is reinforced in the foraging behavior of fruit flies to improve the convergence rate of FOA. Second, an elimination mechanism is added to FOA to increase the diversity. Third, a reverse operator and a multiplication operator are proposed. They are performed on the solution sequence in the fruit fly's smell search and vision search processes, respectively. In the experiment, 10 benchmarks selected from TSPLIB are tested. The results show that the improved FOA outperforms other alternatives in terms of the convergence rate and precision.

关键词： Traveling salesman problem Fruit fly optimization algorithm Elimination mechanism Vision search Operator

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A NEW CASCADING FAILURE MODEL WITH DELAY TIME IN CONGESTED COMPLEX NETWORKS

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Journal of Systems Science and Systems engineering 2009年第3期18卷 369-381页

作者： Jian WANG Yanheng LIU Yu JIAO College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

Cascading failures often occur in congested complex networks. Cascading failures can be expressed as a three-phase process： generation, diffusion, and dissipation of congestion. Different from the betweenness centrality, a congestion function is proposed to represent the extent of congestion on a given node. Inspired by the restart process of a node, we introduce the concept of ＂delay time,＂ during which the overloaded node Cannot receive or forward any traffic, so an intergradation between permanent removal and nonremoval is built and the flexibility of the presented model is demonstrated. Considering the connectivity of a network before and after cascading failures is not cracked because the overloaded node are not removed from network permanently in our model, a new evaluation function of network efficiency is also proposed to measure the damage caused by cascading failures. Finally, we investigate the effects of network structure and size, delay time, processing ability, and traffic generation speed on congestion propagation. Cascading processes composed of three phases and some factors affecting cascade propagation are uncovered as well.

关键词： Complex networks cascading failures congestion effects propagation model

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A combined collective classification method for sparse data

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Journal of Information and computational Science 2011年第15期8卷 3453-3461页

作者： Li, Lina Ouyang, Jihong Liu, Dayou Yang, Bo Huang, Yuxiao Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology JiLin University Changchun 130012 China

Collective classification in networked data has become an important and active research topic, it has a wide variety of real world applications, such as hyperlinked document classification, protein interaction and gene expression data classification, recommending system analysis. However, previous work has shown that the performance of collective classification can degrade when there are too few labels available. In this paper, we firstly present a novel mix collective classification framework that combines network structure features with label distribution of nodes to perform this task, then propose a new method to implement this framework. Through experiments on four real world datasets, we demonstrate that our method outperforms the other state-of-art approaches for this problem. © 2009 by Binary Information Press.

关键词： Information retrieval systems

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A Heuristic Clustering Algorithm for Mining Communities in Signed Networks

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Journal of Computer Science & Technology 2007年第2期22卷 320-328页

作者：杨博刘大有 College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

Signed network is an important kind of complex network, which includes both positive relations and negative relations. Communities of a signed network are defined as the groups of vertices, within which positive relations are dense and between which negative relations are also dense. Being able to identify communities of signed networks is helpful for analysis of such networks. Hitherto many algorithms for detecting network communities have been developed. However, most of them are designed exclusively for the networks including only positive relations and are not suitable for signed networks. So the problem of mining communities of signed networks quickly and correctly has not been solved satisfactorily. In this paper, we propose a heuristic algorithm to address this issue. Compared with major existing methods, our approach has three distinct features. First, it is very fast with a roughly linear time with respect to network size. Second, it exhibits a good clustering capability and especially can work well with complex networks without well-defined community structures. Finally, it is insensitive to its built-in parameters and requires no prior knowledge.

关键词： complex network signed network community clustering graph theory

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Lightweight axiom pinpointing via replicated driver and customized SAT-solving

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Frontiers of Computer Science 2023年第2期17卷 121-133页

作者： Dantong OUYANG Mengting LIAO Yuxin YE College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering(Jilin University) Ministry of EducationChangchun 130012China

In description logic,axiom pinpointing is used to explore defects in ontologies and identify hidden justifications for a logical *** recent years,SAT-based axiom pinpointing techniques,which rely on the enumeration of minimal unsatisfiable subsets(MUSes)of pinpointing formulas,have gained increasing *** with traditional Tableau-based reasoning approaches,SAT-based techniques are more competitive when computing justifications for consequences in large-scale lightweight description logic *** this article,we propose a novel enumeration justification algorithm,working with a replicated *** replicated driver discovers new justifications from the explored justifications through cheap literals resolution,which avoids frequent calls of SAT ***,when the use of SAT solver is inevitable,we adjust the strategies and heuristic parameters of the built-in SAT solver of axiom pinpointing *** adjusted SAT solver is able to improve the checking efficiency of unexplored *** proposed method is implemented as a tool named *** experimental results show that RDMinA outperforms the existing axiom pinpointing tools on practical biomedical ontologies such as Gene,Galen,NCI and Snomed-CT.

关键词： axiom pinpointing description logic SAT solver

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Modal extension rule

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Progress in Natural Science:Materials International 2005年第6期15卷 550-558页

作者： WU Xia*, SUN Jigui, LIN Hai and FENG Shasha(College of Computer Science and Technology, Jilin University, Changchun 130012, China key laboratory for symbolic computation and knowledge engineering, Jilin University, Changchun 130012, China) College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory for Symbolic Computation and Knowledge Engineering Jilin University Changchun 130012 China

Modal logics are good candidates for a formal theory of agents. The efficiency of reasoning method in modal logics is very important, because it determines whether or not the reasoning method can be widely used in systems based on agent. In this paper, we modify the extension rule theorem proving method we presented before, and then apply it to P-logic that is translated from modal logic by functional transformation. At last, we give the proof of its soundness and completeness.

关键词： theorem proving extension rule modal logics functional transformation.

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Neural Explainable Recommender Model Based on Attributes and Reviews

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Journal of Computer Science & Technology 2020年第6期35卷 1446-1460页

作者： Yu-Yao Liu Bo Yang Hong-Bin Pei Jing Huang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China

Explainable recommendation, which can provide reasonable explanations for recommendations, is increasingly important in many fields. Although traditional embedding-based models can learn many implicit features, resulting in good performance, they cannot provide the reason for their recommendations. Existing explainable recommender methods can be mainly divided into two types. The first type models highlight reviews written by users to provide an explanation. For the second type, attribute information is taken into consideration. These approaches only consider one aspect and do not make the best use of the existing information. In this paper, we propose a novel neural explainable recommender model based on attributes and reviews (NERAR) for recommendation that combines the processing of attribute features and review features. We employ a tree-based model to extract and learn attribute features from auxiliary information, and then we use a time-aware gated recurrent unit (T-GRU) to model user review features and process item review features based on a convolutional neural network (CNN). Extensive experiments on Amazon datasets demonstrate that our model outperforms the state-of-the-art recommendation models in accuracy of recommendations. The presented examples also show that our model can offer more reasonable explanations. Crowd-sourcing based evaluations are conducted to verify our model's superiority in explainability.

关键词： recommender system explainable recommendation review usefulness attribute usefulness

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