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IEEE Vehicular Technology Conference

作者： Geng Sun Yanheng Liu Zhaoyu Chen Shuang Liang Qianao Ju Ying Zhang Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China Jilin University College of Computer Science and Technology Changchun China Department of Information Technology Changchun Vocational Institute of Technology Changchun China Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA

ISBN: (纸本)9781538663592;9781538663585

The sparse synthesis of the concentric circular antenna array (CCAA) is a very important technology because it is able to reduce the cost of the antenna array. In this paper, we first formulate a multi-objective optimization problem to jointly reduce the maximum sidelobe level (SLL) and the number of the switched-on elements of the CCAA. Then, we propose a novel enhanced non-dominated sorting genetic algorithm-II (ENSGA-II) to solve this problem. ENSGA-II introduces a hierarchy mechanism to improve the population utilization of the conventional non-dominated sorting genetic algorithm, thereby enhancing the accuracy and the convergence rate of the algorithm. Simulation results show that ENSGA-II obtains a lower maximum SLL with the similar number the switched-off elements compared with other algorithms. Moreover, ENSGA-II has a faster convergence rate.

关键词： antenna arrays genetic algorithms Antenna arrays Genetic Algorithm (GA) Loop antennas side lobe level Elements Concentric Small B-Cell Lymphocytic Lymphoma evolutionary algorithm

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An unmanned aerial vehicle pose estimation system based on SLAM

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IOP Conference Series: Materials Science and engineering 2019年第4期569卷

作者： Xinglei Dou Yuzhou Huo Yongchang Liu Xin Wang College of Software Engineering Jilin University Changchun Jilin 130012 China College of Computer Science and Technology Jilin University Changchun Jilin 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun Jilin 130012 China

This paper presents an unmanned aerial vehicle (UAV) pose estimation system based on monocular simultaneous localization and mapping (SLAM) guided by the desired shot. The system enables UAV to automatically adjust the pose to achieve a shot close to the desired shot provided by the user. The SLAM module in the system includes ORB feature-based visual odometry and Levenberg-Marquardt method-based optimizer. To ensure the reliability of the camera pose estimation result, the bag of words model is used to select an image which has enough good matches with the desired shot. The experimental results prove that the system is valid and effective.

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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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Tuning the Learning Rate for Stochastic Variational Inference

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Journal of Computer Science & Technology 2016年第2期31卷 428-436页

作者： Xi-Ming Li Ji-Hong Ouyang College of Computer Science and Technology Jilin University Changchun 130000 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130000 China

Stochastic variational inference （SVI） can learn topic models with very big corpora. It optimizes the variational objective by using the stochastic natural gradient algorithm with a decreasing learning rate. This rate is crucial for SVI; however, it is often tuned by hand in real applications. To address this, we develop a novel algorithm, which tunes the learning rate of each iteration adaptively. The proposed algorithm uses the Kullback-Leibler （KL） divergence to measure the similarity between the variational distribution with noisy update and that with batch update, and then optimizes the learning rates by minimizing the KL divergence. We apply our algorithm to two representative topic models： latent Dirichlet allocation and hierarchical Dirichlet process. Experimental results indicate that our algorithm performs better and converges faster than commonly used learning rates.

关键词： stochastic variational inference online learning adaptive learning rate topic model

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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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Zero-shot user intent detection via capsule neural networks

arXiv

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arXiv 2018年

作者： Xia, Congying Zhang, Chenwei Yan, Xiaohui Chang, Yi Yu, Philip S. Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States Huawei Technologies San JoseCA95050 United States College of Artificial Intelligence Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China

User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users' utterances as intents are diversely expressed and novel intents will continually be involved. Instead, we study the zero-shot intent detection problem, which aims to detect emerging user intents where no labeled utterances are currently available. We propose two capsule-based architectures: IntentCapsNet that extracts semantic features from utterances and aggregates them to discriminate existing intents, and IntentCapsNet-ZSL which gives IntentCapsNet the zero-shot learning ability to discriminate emerging intents via knowledge transfer from existing intents. Experiments on two real-world datasets show that our model not only can better discriminate diversely expressed existing intents, but is also able to discriminate emerging intents when no labeled utterances are available. Copyright © 201., The Authors. All rights reserved.

关键词： knowledge management

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Event-based structural change detection in urban-scale contact network 31

Event-based structural change detection in urban-scale conta...

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31.t AAAI Conference on Artificial Intelligence, AAAI 201.

作者： Bai, Yuan Yang, Bo Eggo, Rosalind M. Du, Zhanwei College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China London School of Hygiene and Tropical Medicine LondonWC1E7HT United Kingdom Department of Integrative Biology University of Texas at Austin Austin78705 United States

ISBN: (纸本)9781577357865

The detection of structural changes is an important task in analyzing network evolution, especially for interactions between people, that may be driven by external events. Existing work relies on snapshot data and misses out some key functions of networks. Here, we study contact network evolution where no snapshot data are available. In spite of the challenge, this study demonstrates how contact networks can be used to predict and control infectious disease epidemics. We first model structural changes in contact networks during the 2009 influenza pandemic in Hong Kong, and then present a probabilistic framework to address it, aiming to answer when and how the underlying structure changes, utilizing multiple data sources including demographic data, and epidemic surveillance data. The efficacy and public health utility of the method are demonstrated using both synthetic and real data. © 201., Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Disease control

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Efficient Image Transmission Schemes over Zigbee-Based Image Sensor Networks

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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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Iris Recognition Based on Adaptive Gabor Filter 12th

Iris Recognition Based on Adaptive Gabor Filter

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1.th Chinese Conference on Biometric Recognition, CCBR 201.

作者： Liu, Shuai Liu, Yuanning Zhu, Xiaodong Huo, Guang Cui, Jingwei Chen, Yihao Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China School of Software Jilin University ChangchunJilin130012 China School of Computer Science and Technology Jilin University ChangchunJilin130012 China Informatization Office Northeast Electric Power University Jilin132012 China

ISBN: (纸本)9783319699226

Aiming at the problem of multi-category iris recognition, there proposes a method of iris recognition algorithm based on adaptive Gabor filter. Use DE-PSO to adaptive optimize the Gabor filter parameters. DE-PSO is composed of particle swarm optimization and differential evolution algorithm. Use 1. groups of 2D-Gabor filters with different frequencies and directions to process iris images. According to the direction and frequency of maximum response amplitude, transform iris features into 51.-bit binary feature encoding. Calculate the Hamming distance of feature code and compare with the classification threshold, determine iris the type of iris. Experiment on a variety of iris databases with multiple Gabor filter algorithms, the results showed that this algorithm has higher recognition rate, the ROC curve is closer to the coordinate axis and the robustness is better, compare with other Gabor filter algorithm. © 201., Springer International Publishing AG.

关键词： Hamming distance

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Sparse hybrid variational-Gibbs algorithm for latent Dirichlet allocation 16

Sparse hybrid variational-Gibbs algorithm for latent Dirichl...

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1.th SIAM International Conference on Data Mining 201., SDM 201.

作者： Li, Ximing Ouyang, Jihong Zhou, Xiaotang College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)9781510828117

Topic modeling algorithms such as the latent Dirichlet allocation (LDA) play an important role in machine learning research. Fitting LDA using Gibbs sampler-related algorithms involves a sampling process over K topics. We can use the sparsity in LDA to accelerate this expensive topic sampling process even for very large K values. However, LDA gradually loses sparsity as the number of documents increases. Motivated by the goal of fast LDA inference with large numbers of both topics and documents, in this paper we propose the novel sparse hybrid variational-Gibbs (SHVG) algorith-m. The SHVG algorithm divides the topic sampling probability into a sparse term that scales linearly with the number of per-document instantiated topics Kd, and a dense term that uses the Alias method to reduce the time cost to constant 0(1. time. This will lead to a significant improvement on efficiency. Using stochastic optimization techniques, we further develop an online version of SHVG for streaming documents. Experimental results on corpora with a wide range of sizes demonstrate the efficiency and effectiveness of the proposed SHVG algorithm. Copyright © by SIAM.

关键词： Efficiency

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