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The Equivalent Representation of the Breadth-One D-Invariant Polynomial Subspace and Its Discretization

Journal of Systems Science & Complexity 2016年第5期29卷 1436-1445页

作者： JIANG Xue ZHANG Shugong Institute of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering (Ministry of Education) Jilin University

This paper demonstrates the equivalence of two classes of D-invariant polynomial subspaces, i.e., these two classes of subspaces are different representations of the breadth-one D-invariant subspace. Moreover, the authors solve the discrete approximation problem in ideal interpolation for the breadth-one D-invariant subspace. Namely, the authors find the points, such that the limiting space of the evaluation functionals at these points is the functional space induced by the given D-invariant subspace, as the evaluation points all coalesce at one point.

关键词： Breadth-one D-invariant polynomial subspace discrete approximation problem idealinterpolation.

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Low cost edge sensing for high quality demosaicking

arXiv

引用

arXiv 2018年

作者： Niu, Yan State Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China

Digital cameras that use Color Filter Arrays (CFA) entail a demosaicking procedure to form full RGB images. As today's camera users generally require images to be viewed instantly, demosaicking algorithms for real applications must be fast. Moreover, the associated cost should be lower than the cost saved by using CFA. For this purpose, we revisit the classical Hamilton-Adams (HA) algorithm, which outperforms many sophisticated techniques in both speed and accuracy. Based on a close look at HA's strength and weakness, we design a very low cost edge sensing scheme. Briefly, it guides demosaicking by a logistic functional of the difference between directional variations. We extensively compare our algorithm with 28 demosaicking algorithms by running their open source codes on benchmark datasets. Compared to methods of similar computational cost, our method achieves substantially higher accuracy;Whereas compared to methods of similar accuracy, our method has significantly lower cost. Moreover, on test images of currently popular resolution, the quality of our algorithm is comparable to top performers, whereas its speed is tens of times faster. Copyright © 2018, The Authors. All rights reserved.

关键词： Costs

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A novel Bayesian network-based fault prognostic method for semiconductor manufacturing process

A novel Bayesian network-based fault prognostic method for s...

引用

2017 IEEE International Conference on Industrial Technology, ICIT 2017

作者： Wang, Guodong Hasani, Ramin M. Zhu, Yungang Grosu, Radu Institute of Computer Engineering Vienna University of Technology Austria College of Computer Science and Technology Jilin University China State Key Laboratory of Applied Optics Chinese Academy of Sciences China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

ISBN: (纸本)9781509053209

Fault prognostic in various levels of production of semiconductor chips is considered to be a great challenge. To reduce yield loss during the manufacturing process, tool abnormalities should be detected as early as possible during process monitoring. In this paper, we propose a novel fault prognostic method based on Bayesian networks. The network is designed such that it can process both discrete and continuous variables, to represent the correlations between critical deviations and to process quality control data based on divide-and-conquer strategy. Such a network enables us to perform high-precision multi-step prognostic on the status of the fabrication process given the current state of the sensory info. Additionally, we introduce a layer-wise approach for efficient learning of the Bayesian-network parameters. We evaluate the accuracy of our prognostic model on a wafer fabrication dataset where our model performs precise next-step fault prognostic by using the control sensory data. © 2017 IEEE.

关键词： Bayesian networks

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Multi-objective optimization for distributed collaborative beamforming in mobile wireless sensor networks

Multi-objective optimization for distributed collaborative b...

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IEEE Symposium on Computers and Communications

作者： Geng Sun Yanheng Liu Guojun Shen Aimin Wang Ying Zhang Victor C. M. Leung School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

Mobile wireless sensor networks (MWSN) are resource constrained, and have limited energy and transmission range. Distributed collaborative beamforming (DCB) in MWSN based on a virtual node antenna array (VNAA) can increase the transmission distance and enhance energy efficiency of a single sensor node. To achieve a lower maximum sidelobe level (SLL), sensor nodes can move to optimal locations with optimal excitation currents for DCB. However, this leads to an extra motion energy consumption. In this paper, we construct a multi-objective optimization framework to jointly optimize the maximum SLL, the transmission power and the motion energy consumption of the DCB nodes in MWSN. Moreover, an improved non-dorminated sorting genetic algorithm-II (INSGA-II) is proposed for solving the optimization problem. Simulation results show that the maximum SLL, the transmission power and the motion energy consumption of the VNAA can be effectively optimized by the proposed algorithms.

关键词： Mobile wireless sensor networks Collaborative beamforming Antenna arrays Genetic algorithm

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Sparse synthesis of concentric circular antenna array via multi-objective evolutionary computation

Sparse synthesis of concentric circular antenna array via mu...

引用

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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Iris Recognition Based on Adaptive Gabor Filter 12th

Iris Recognition Based on Adaptive Gabor Filter

引用

12th Chinese Conference on Biometric Recognition, CCBR 2017

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 16 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 512-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. © 2017, Springer International Publishing AG.

关键词： Hamming distance

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Zero-shot user intent detection via capsule neural networks

arXiv

引用

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 © 2018, The Authors. All rights reserved.

关键词： knowledge management

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Tuning the Learning Rate for Stochastic Variational Inference

引用

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

引用

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

引用

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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