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Efficient Image Transmission Schemes over Zigbee-Based Image Sensor Networks

Chinese Journal of Electronics 2016年第2期25卷 284-289页

作者： TAO Dan YANG Guangwei CHEN Houjin WU Hao LIU Pingping School of Electronic and Information Engineering Beijing Jiaotong University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University College of Computer Science and Technology Jilin University

Image transmission is one of the biggest challenges in wireless sensor networks because of the limited resource on sensor nodes. We proposed two image transmission schemes driven by reliability and real time considerations in order to transfer JPEG images over Zigbee-based sensor networks. By adding two bytes counter in the header of data packet, we can easily solve the repeated data reception problem caused by retransmission mechanism in traditional Zigbees network layer. We proposed an efficient retransmission and acknowledgment mechanism in Zigbees application layer. By classifying different data reception response events, we can provide data packets with differential responses and ensure that image packets can be transferred quickly even with large maximum number of retransmission. Practical results show the effectiveness of our solutions to make image transmission over Zigbee-based sensor networks efficient.

关键词： Image sensor networks Image transmission Retransmission and acknowledgment mechanism Zigbee

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SPHLU:An Efficient Algorithm for Processing PRkNN Queries on Uncertain Data

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Chinese Journal of Electronics 2016年第3期25卷 403-406页

作者： WANG Shengsheng LI Yang CHAI Sheng BOLOU Bolou Dickson College of Computer Science and Technology Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of EducationJilin University

Query on uncertain data has received much attention in recent years, especially with the development of Location-based services(LBS). Little research is focused on reverse k nearest neighbor queries on uncertain data. We study the Probabilistic reverse k nearest neighbor(PRkN N) queries on uncertain data. It is succinctly shown that, PRkN N query retrieves all the points that have higher probabilities than a given threshold value to be the Reverse k-nearest neighbor(RkN N) of query data *** previous works on this topic mostly process with k > *** algorithms allow the cases for k > 1, but the efficiency is inefficient especially for large k. We propose an efficient pruning algorithm — Spatial pruning heuristic with louer and upper bound(SPHLU) for solving the PRkN N queries for k > 1. The experimental results demonstrate that our algorithm is even more efficient than the existent algorithms especial for a large value of k.

关键词： Spatial database Uncertain data R-tree Probabilistic reverse k nearest neighbor queries

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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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Model counting with boolean algebra and extension rule

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Journal of Convergence Information Technology 2010年第7期5卷 7-7页

作者： Xu, Youjun Ouyang, Dantong Ye, Yuxin College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Model counting is an important problem in artificial intelligence and is applied in several areas of information science. Extension rule is a method which could be used to count models. But it's not appropriate when clause length is short or clause number is huge. After studying extension rule, we found that the satisfiability problem could be solved by hitting set algorithms. And the models could be counted with extension rule after calculating hitting sets of a clause set. Therefore, we proposed an algorithm MCBE in this paper. With Boolean algebra, MCBE could easily calculate hitting sets of a clause set. Then, it gives the number of models with extension rule. The test results show that when clause length is short and clause number is big enough, the algorithm is more efficiency than the algorithm CDP and CER.

关键词： Boolean algebra

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A novel combine forecasting method for predicting news update time

Journal of Software

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Journal of Software 2012年第12期7卷 2787-2793页

作者： Wang, Mengmeng Zuo, Xianglin Zuo, Wanli Wang, Ying College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin Unversity Changchun China

With the rapid development of Internet, information provided by the Internet has shown explosive growth. In the face of massive and constantly updated information on the Internet, how the user can fast access to more valuable and more information has become one of the hot spots. The time of Web Page update appears to be erratic, so forecasting the update time of news reports is even more difficult. From the view of application, we can use mathematical models to maximize the approximation of variation, although it cannot be completely accurate. So is the predicting the update time of news which helps in improving the news crawler's scheduling policy. In this paper, we proposed a combined predict algorithm for news update. In order to predict the update time of news, firstly, we applied the Exponential Smoothing method to our dataset, and we also have selected the optimal parameters. Secondly, we leveraged the Naive Bayes Model for prediction. Finally, we combined two methods for Combination Forecasting, as well as made a compare with former methods. Through the experiments on Sohu News, we show that Combination Forecasting method outperforms other methods while estimating localized rate of updates. © 2012 ACADEMY PUBLISHER.

关键词： Websites

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LLA: A New High Precision Mobile Node Localization Algorithm Based on TOA

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Journal of Communications 2010年第8期5卷 604-611页

作者： Li, Ruixue Fang, Zhiyi College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Mobile node localization is one of the challenging and crucial issues in wireless sensor networks. The paper proposed a new approach to mobile localization, called LLA (Lee Localization Algorithm), to mitigate TOA measurement errors. The algorithm was divided into three phases: (1) pre-ranging algorithm was proposed to initialize localization by employing TOA method;(2) refinement algorithm was designed to filter out bad nodes;(3) smallest circle covering algorithm was developed to deal with good nodes to complete the localization. Experimental results show that the presented approach efficiently eliminates the effects of the measurement bias and obtains a high accuracy of 2m as well as low complexity, outperforming traditional trilateration. © 2010 ACADEMY PUBLISHER.

关键词： Sensor nodes

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Deriving all minimal consistency-based diagnosis sets using SAT solvers

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Progress in Natural Science:Materials International 2009年第4期19卷 489-494页

作者： Xiangfu Zhao a,b, Liming Zhang a,b, Dantong Ouyang a,b, Yu Jiao a,b a key laboratory of symbolic computation and knowledge engineering of Ministry of Education, Jilin University, Changchun 130012, China b School of Computer Science and Technology, Jilin University, Changchun 130012, China [a]Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China [b]School of Computer Science and Technology Jilin University Changchun 130012 China

In this paper, a novel method is proposed for judging whether a component set is a consistency-based diagnostic set, using SAT solv- ers. Firstly, the model of the system to be diagnosed and all the observations are described with conjunctive normal forms (CNF). Then, all the related clauses in the CNF files to the components other than the considered ones are extracted, to be used for satisfiability checking by SAT solvers. Next, all the minimal consistency-based diagnostic sets are derived by the CSSE-tree or by other similar algorithms. We have implemented four related algorithms, by calling the gold medal SAT solver in SAT07 competition – RSAT. Experimental results show that all the minimal consistency-based diagnostic sets can be quickly computed. Especially our CSSE-tree has the best effciency for the singleor double-fault diagnosis.

关键词： Conjunctive normal form Consistency-based diagnosis Model-based diagnosis SAT solver

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Revisit the logical difference of description logic terminologies

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Journal of Convergence Information Technology 2011年第4期6卷 240-245页

作者： Feng, Shasha Ouyang, Dantong Liu, Jie College of Computer Science and Technology Jilin University Key Lab of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China

The logical difference is important to ontology engineers in capturing and understanding the difference between different versions of given ontology. For acyclic EL terminologies, in which the well applied medical ontology SNOMED CT is represented, there are two methods proposed in computing the logical difference between terminologies: direct computation method and uniform interpolant method. We argue that the later method outperforms the former one in showing the dependency between entailments in the logical difference through the introduction of concept difference. The resulting logical difference conveys more information to ontology engineers than direct computation method.

关键词： Ontology

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Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training

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ACM Transactions on knowledge Discovery from Data 2024年第9期18卷 1-30页

作者： Liu, Yonghao Li, Mengyu Li, Ximing Huang, Lan Giunchiglia, Fausto Liang, Yanchun Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China University of Trento Trento Italy Zhuhai Lab. of the Key Lab. of Symbolic Computation and Knowledge Eng. of the Ministry of Education Zhuhai College of Science and Technology Zhuhai China

Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning with graph neural networks to solve few-shot node classification on graphs. Despite their promising performance, some limitations remain. First, they employ the node encoding mechanism of homophilic graphs to learn node embeddings, even in heterophilic graphs. Second, existing models based on meta-learning ignore the interference of randomness in the learning process. Third, they are trained using only limited labeled nodes within the specific task, without explicitly utilizing numerous unlabeled nodes. Finally, they treat almost all sampled tasks equally without customizing them for their uniqueness. To address these issues, we propose a novel framework for few-shot node classification called Meta-GPS. Specifically, we first adopt an efficient method to learn discriminative node representations on homophilic and heterophilic graphs. Then, we leverage a prototype-based approach to initialize parameters and contrastive learning for regularizing the distribution of node embeddings. Moreover, we apply self-Training to extract valuable information from unlabeled nodes. Additionally, we adopt S (scaling and shifting) transformation to learn transferable knowledge from diverse tasks. The results on real-world datasets show the superiority of Meta-GPS. Our code is available here. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Local feature aggregation algorithm based on graph convolutional network

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Frontiers of Computer Science 2022年第3期16卷 203-205页

作者： Hao WANG Liyan DONG Minghui SUN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China

1Introduction and main contributions In the field of social networks and knowledge graphs,semi-supervised learning models based on graph convolutional networks have achieved great success in node classification[1],inductive node embedding[2],link prediction[3],and *** semi-supervised models based on graph convolutional network(GCN)[4]expect to obtain more feature information of a graph or accelerate the training.

关键词： convolution aggregation semi

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