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DiagDO: an efficient model based diagnosis approach with multiple observations

Frontiers of Computer Science 2023年第6期17卷 125-134页

作者： Huisi ZHOU Dantong OUYANG Xinliang TIAN Liming ZHANG Laboratory of Symbolic Computation and Knowledge Engineering Jilin UniversityChangchun 130000China College of Computer Science and Technology Jilin UniversityChangchun 130000China

Model-based diagnosis(MBD)with multiple observations shows its significance in identifying fault *** existing approaches for MBD with multiple observations use observations which is inconsistent with the prediction of the *** this paper,we proposed a novel diagnosis approach,namely,the Diagnosis with Different Observations(DiagDO),to exploit the diagnosis when given a set of pseudo normal observations and a set of abnormal *** ideas are proposed in this ***,for each pseudo normal observation,we propagate the value of system inputs and gain fanin-free edges to shrink the size of possible faulty ***,for each abnormal observation,we utilize filtered nodes to seek surely normal ***,we encode all the surely normal components and parts of dominated components into hard clauses and compute diagnosis using the MaxSAT solver and MCS *** tests on the ISCAS'85 and ITC'99 benchmarks show that our approach performs better than the state-of-the-art algorithms.

关键词： model based diagnosis maximum satisfiability top-level diagnosis cardinality-minimal diagnosis subset-minimal diagnosis

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A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

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Computers, Materials & Continua 2024年第6期79卷 4349-4370页

作者： Jiajie Shen Yan Wang Dongxu Zhang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of EducationColleague of Computer Science and TechnologyJilin UniversityChangchun130012China College of Software and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun130012China

Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ***,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion *** to the similarities between the information of the transitions and their adjacent steady ***,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as *** address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor *** classifier in these levels is composed of multiple-LSTM layers and an attention *** introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating *** method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions *** results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,*** is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.

关键词： Lower-limb prosthetics deep neural networks motion classification

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Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network

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Journal of Computer Science & Technology 2006年第3期21卷 393-400页

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

Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman＇s algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.

关键词： incremental algorithm community structure dynamic network

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NADARAYA-WATSON ESTIMATORS FOR REFLECTED STOCHASTIC PROCESSES

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Acta Mathematica Scientia 2024年第1期44卷 143-160页

作者：韩月才张丁文 School of Mathematics Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China

We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential *** estimates,based on either the continuously observed process or the discretely observed process,are *** certain conditions,we prove the strong consistency and the asymptotic normality of the two *** method is also suitable for one-sided reflected stochastic differential *** results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.

关键词： reflected stochastic differential equation discretely observed process continuously observed process Nadaraya-Watson estimator asymptotic behavior

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Autonomous Domain Correlation-Based Cross-domain Network View Caching Method for SDN Distributed Controller

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Chinese Journal of Electronics 2016年第6期25卷 1127-1133页

作者： FU Tao HU Liang CHAI Sheng CHE Xilong College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Jilin University

Software-defined networks(SDN) maintain a global view of the network, thus improving the intelligence of forwarding decisions. With the expansion of the network scale, distributed controllers are used in a variety of large-scale networks in which subnetworks managed by controller instance are called autonomous domains. We analyze statistic frequency of communication across the autonomous domain. We calculate the autonomous domain correlations for controller instances using acquired statistical information. We cache network views to highly correlated controller instances. Distributed controllers are capable of considering both the average response time and overall storage. An experiment shows that our method can fully take advantage of these two performance indicators.

关键词： Software-defined networks(SDN) Distributed controller Network analyze Network view cache

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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 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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Science Letters:Model for cascading failures in congested Internet

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Journal of Zhejiang university-Science A(Applied Physics & engineering) 2008年第10期9卷 1331-1335页

作者： Jian WANG Yan-heng LIU Jian-qi ZHU Yu JIAO Key Lab of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China

Cascading failures often occur in congested networks such as the Internet. A cascading failure can be described as a three-phase process： generation, diffusion, and dissipation of the congestion. In this account, we present a function that represents the extent of congestion on a given node. This approach is different from existing fimctions based on betweenness centrality. By introducing the concept of ＇delay time＇, we designate an intergradation between permanent removal and nonremoval. We also construct an evaluation fimction of network efficiency, based on congestion, which measures the damage caused by cascading failures. Finally, we investigate the effects of network structure and size, delay time, processing ability and packet generation speed on congestion propagation. Also, we uncover the relationship between the cascade dynamics and some properties of the network such as structure and size.

关键词： Complex network Cascading failures Congestion effects Propagation model

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A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models

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Computer Modeling in engineering & Sciences 2020年第7期124卷 303-313页

作者： Yan Li Dayou Liu Yungang Zhu Jie Liu College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China

Under different conditions,gene regulatory networks(GRNs)of the same gene set could be similar but *** differential analysis of GRNs under different conditions is important for understanding condition-specific gene regulatory *** a naive approach,existing GRN inference algorithms can be used to separately estimate two GRNs under different conditions and identify the differences between ***,in this way,the similarities between the pairwise GRNs are not taken into *** joint differential analysis algorithms have been proposed recently,which were proved to outperform the naive approach *** this paper,we model the GRNs under different conditions with structural equation models(SEMs)to integrate gene expression data and genetic perturbations,and re-parameterize the pairwise SEMs to form an integrated model that incorporates the differential ***,a Bayesian inference method is used to make joint differential analysis by solving the integrated *** evaluated the performance of the proposed re-parametrization-based Bayesian differential analysis(ReBDA)algorithm by running simulations on synthetic data with different *** performance of the ReBDA algorithm was demonstrated better than another state-of-the-art joint differential analysis algorithm for SEMs ReDNet *** the end,the ReBDA algorithm was applied to make differential analysis on a real human lung gene data set to illustrate its applicability and practicability.

关键词： Gene regulatory networks structural equation models joint

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A randomized diversification strategy for solving satisfiability problem with long clauses

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Science China(Information Sciences) 2017年第9期60卷 121-131页

作者： Jian GAO Ruizhi LI Minghao YIN College of Computer Science Northeast Normal University College of Information Science and Technology Dalian Maritime University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

Satisfiability problem(SAT) is a central problem in artificial intelligence due to its computational complexity and usefulness in industrial applications. Stochastic local search(SLS) algorithms are powerful to solve hard instances of satisfiability problems, among which CScore SAT is proposed for solving SAT instances with long clauses by using greedy mode and diversification mode. In this paper, we present a randomized variable selection strategy to improve efficiency of the diversification mode, and thus propose a new SLS *** perform a number of experiments to evaluate the new algorithm comparing with the recently proposed algorithms, and show that our algorithm is comparative with others for solving random instances near the phase transition threshold.

关键词： SAT local search randomized diversification strategy phase transition long clause

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