检索结果-内蒙古大学图书馆

Speech separation in the vehicle environment based on fastICA algorithm

Journal of Multimedia 2012年第1期7卷 33-40页

作者： Zhang, Jindong Qin, Guihe Liu, Ye College of computer science and technology Jilin University Changchun China Key Laboratory of Symbol Computation Knowledge Engineering of the Ministry of Education Changchun China

The speech interaction in-vehicle was mainly realized by the speech recognition. The human-machine interaction around was usually disturbed by the noise, and the speech received by the receiver was not the original pure speech, so compared to the pure environment, the accuracy of the speech recognition declined so sharply that it could not meet the demand of the practical application of human- machine interaction. So the speech recognition was required to have the strong adaptability and processing capacity to the speech with noise. In this paper, the FastICA algorithm in signal process and statistics was studied and used to separate the driver's speech in the vehicle environment, and realizes the pretreatment in recognizing driver's speech. The effectiveness of the method had been validated in actual vehicle experimental configuration. © 2012 Academy Publisher.

关键词： Speech processing

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A Chinese online document clustering algorithm based on hidden sentiment vector

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Journal of computational Information Systems 2011年第6期7卷 1974-1981页

作者： Yang, Hui Wang, Zhe Zhou, Xu Wang, Zuo Zhou, Tong College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory for Symbol Computation and Knowledge Engineering Ministry of Education China

The core idea of clustering algorithm is the division of data into groups of similar objects. Some clustering algorithms are proven good performance on document clustering, such as k-means and UPGMA etc. However, few document clustering algorithms pay attention to the hidden sentiment which is a very important feature of the documents. This paper presents an improved k-means algorithm (HSK-Means) based on hidden sentiment vector for Chinese document clustering. Chinese dependency grammar rules are used to extract document feature and evaluate hidden sentiment. A new method for selecting initial cluster centroids based on rank mechanism of similarity is also proposed to improve accuracy of clustering algorithm. The experimental results on real online document sets illustrate that the performance of HSK-Means algorithm is better than classic document clustering algorithms. Copyright © 2011 Binary Information Press.

关键词： Information retrieval

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Combining Greedy Method and Genetic Algorithm to Identify Transcription Factor Binding Sites

Combining Greedy Method and Genetic Algorithm to Identify Tr...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： Wanyu Wang Xiaoyu Chang Chunguang Zhou College of Computer Science Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

Identification of Transcription Factor Binding Sites (TFBS) from the upstream region of genes remains a highly important and unsolved problem particularly in higher eukaryotic genomes. In this paper, we propose a novel approach to identify transcription factor binding sites. This approach combines greedy method and genetic algorithm (CGGA) to search conserved segment in the given sequence set. A new greedy method which can efficiently search a local optimal result is proposed. In order to solve the high complexity of this algorithm, we also give an effective improvement for this method. Then, we describe how to combine genetic algorithm with this greedy method to find the more optimal results. Greedy method is combined to the fitness function of the genetic algorithm. We apply this approach on two different TFBS sets and the results show that it can find correct result both effective and efficient, and for CRP binding sites, it get a more accurate result than Gibbs Sampler, AlignACE and MDGA.

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An efficient and effective similarity measure to enable data mining of fabric image of Xinjiang folk art

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Journal of Information and computational Science 2015年第5期12卷 1695-1702页

作者： Te, Rigen Li, Xiongfei Zhang, Yueying Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun China College of Materials Science and Engineering Jilin University Changchun China

Fabric pattern is a folk art of artificial fabrics pattern design which includes apparels (known as clothes and hats), and crafts (known as carpets and tapestries). Although image processing, information retrieval and data mining have made a large impact on many human endeavors, they never had an impact on the study of the fabric image. In this paper we will present the reasons for this, and introduce a novel similarity measure and algorithm (Pattern Similarity Measuring Algorithm (PSMA)) which allows efficient data mining for large collections of fabric image. PSMA, the effectiveness of which is proved be experiment test, is an image similarity measurement algorithm that achieves the machine-learning based theory via the gradient descent algorithm with multidimensional linear regression. Copyright © 2015 Binary Information Press.

关键词： Data mining

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Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm 2

Design of a Daily Brief Business Report Generator based on W...

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2nd International Conference on Computer Information Science and Application Technology, CISAT 2019

作者： Lin, Gang Liang, Yanchun Fu, Xiaoyang Chen, Guangshun Cai, Shaocan Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Department of Computer Science and Technology School of Computer Science Zhuhai Guangdong519041 China Department of Computer Science and Technology School of Computer Science Zhuhai College Jilin University Zhuhai Guangdong519041 China

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval, a daily business report generator system based on web scraping with k nearest neighbor (kNN) classification algorithm is proposed in this paper. It covers the web crawler technology that is to access existing website system and extract business data. The kNN algorithm is applied to identify the verification code on the login page, and the brief daily report generating in a spread-sheet style grid. Compared with some OCR engine for image recognition, the system in Python can automatically generate the brief daily business reports by the kNN algorithm, which is better than some library with default training set on validating the verification code. © Published under licence by IOP Publishing Ltd.

关键词： Report generators

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computational Prediction of Human Body-Fluid Protein

Computational Prediction of Human Body-Fluid Protein

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Dan Shao Xueteng Cui Lan Huang Kai He Yan Wang Yao Wang Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University College of Computer Science and Technology Changchun University Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University

ISBN: (纸本)9781728118680

Research on body fluid proteomes has led to the discoveries of context-dependent proteomics profiles and numerous novel disease biomarkers. Common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational efforts have shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first reviewed published computational methods on this topic and then presented a new database system and a novel deep learning-based method for predicting body-fluid proteins in 16 types of human fluids. The results show that our new system outperforms existing methods and provides a highly promising tool to facilitate clinical proteomic discovery.

关键词： machine learning protein prediction body-fluid proteome deep learning Machine Learning Database systems Artificial Intelligence proteomics protein modifications Fluid Human Body maladjustment Learning-based method Biological Markers Body Fluids

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A novel method for RNA secondary structure prediction

A novel method for RNA secondary structure prediction

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2011 7th International Conference on Natural computation, ICNC 2011

作者： Xing, Chong Ji, Zhaohua Wang, Gang Wang, Yao Shen, Wei Liang, Yanchun Key Laboratory for Symbol Computation and Knowledge Engineering of National Education MinistryCollege of Computer Science and TechnologyJilin University Changchun China College of Information Engineering Guanghua College of Changchun University Changchun China College of Communications Engineering Jilin University Changchun China

ISBN: (纸本)9781424499533

In this paper, we propose PSOfold, a particle swarm optimization for RNA secondary structure prediction. PSOfold is based on the recently published IPSO. We present two strategies to improve the performance of IPSO. Firstly, in order to boost the competence in searching an optimal solution, fuzzy logic control is used to adaptively adjust the parameters in PSO. Accordingly, three fuzzy logic controls are designed by which the inertia weight, learning factors and the number of ants are tuned respectively. Secondly, to further settle the stem permutation problem, we put forward a solution conversion strategy (SCS), which can transform discrete values of stems into an ordered stem combination, thereby supplying an enhanced solution to evaluation of objective function. An evaluation of the performance of PSOfold in terms of prediction accuracy is made via comparison with one dynamic programming algorithm mfold and four metaheuristics, IPSO, ACRNA, RnaPredict, SARNA-Predict and mfold for ten individual known structures. PSOfold is able to predict structures with higher prediction accuracy than the other metaheuristic based methods on certain sequences, and has comparable performance compared with mfold. © 2011 IEEE.

关键词： Particle swarm optimization (PSO)

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Medical Image Fusion Based on Sparse Representation and Guided Filtering 4

Medical Image Fusion Based on Sparse Representation and Guid...

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2019 4th Annual International Conference on Information System and Artificial Intelligence, ISAI 2019

作者： Huang, Sa Chu, Guangyu Fei, Yifan Zhang, Xiaoli Wang, Hailiang Department of Radiology Second Hospital Jilin University Changchun Jilin130041 China Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun130012 China Department of Neurosurgery Second Hospital Jilin University Changchun Jilin130041 China

In this paper, we proposed a medical image fusion algorithm based on sparse representation and guided filtering. One of attractive features in the algorithm is that it can preserve the structural information of structural image and color information of functional image. We use a sparse representation in low frequency, initialize the dictionary by using DCT transform, and train the dictionary with each input source image as a training example. It not only ensures time complexity, but also ensures that the low-frequency fusion rules are adaptive. At high frequencies, we use the method of guided filtering to extract structural information from high-frequency images, and use the injection method to fuse high-frequency sub-bands to ensure the validity and richness of structural information. Experimental results show that the proposed fusion algorithm is superior to comparative algorithms in terms of subjective and objective evaluation methods. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Image fusion

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A new simplification method for terrain model using discrete particle swarm optimization 07

A new simplification method for terrain model using discrete...

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Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems

作者： Zhang, Huijie Sun, Jigui Liu, Jin Lv, Nan College of Computer Science and Technology Jilin University NorthEast Normal University Changchun China College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering Changchun China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781595939142

In this paper, we put forward a fast simplification method for the terrain model by integrating the discrete particle swarm optimization with the hierarchical structure. In this method, each particle is represented as a hierarchical structure and corresponds to a candidate solution of the simplified terrain model. In order to acquire an optimal simplified model, we proposed a new model error evaluation function based on the terrain features. Thereby the terrain model yielded by this method has high self-adaptability and optimal simplification ratio. Furthermore, the velocity of the particle is redefined as the probability of the model being simplified superiorly. Therefore, the particles can converge to the optimal simplified model rapidly. The experimental results performed on benchmark terrain data show that the efficiency and the precision of our algorithm are improved greatly and the detailed features in the simplified model are preserved, compared with the others common simplification method. © 2007 ACM.

关键词： Landforms

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Optimization strategy of parallel query processing based on multi-core architecture

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Journal of Convergence Information Technology 2010年第8期5卷 24-24页

作者： Chen, Yongheng Zuo, Wanli He, Fengling Chen, Kerui College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

Chip Multi-Processor (CMP) could support more than two threads to execute simultaneously, and some executing units are owned by each core. Because threads share various resources of CMP, such as L2-Cache, among many threads, CMP system is inherently different from multiprocessors system and, CMP is also different from simultaneously multithreading (SMT). In this paper a novel and complete approach on how to parallelism for relational database multithreaded query execution that strives for maximum resource utilization for both CPU and disk activities. The focus of this approach is on how to use the multithreaded parallel technique to optimize and process queries based on multi-core architecture. A set of algorithms for implementing and optimizing the best query plan, such as the algorithms for scheduling and parallel executing the query plan, the algorithms for allocating thread to sub-query and memory to the buffers between operations in pipelining execution, are proposed in this method. Additionally this paper analysis and optimize the implementation of parallel buffers and multithread. In the experiments, this paper evaluates performance of the parallel buffers and tests the coordination of the multithread.

关键词： Scheduling algorithms

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