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	1. Mining Predicate Association Rule by Gene Expression Programming
	Zuo Jie,tangchangjie,zhangtianqing
	Mechanics 01 July 2004
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	Abstract: Gene expression programming (GEP) is a new technique in genetic computing introduced in 2001. Association rule mining is a typical task in data mining. In this article, a new concept called Predicate Association (PA) is introduced and a new method to discover PA by GEP, called PAGEP (mining Predicate Association by GEP), is proposed. Main results are: (1) The inherent weaknesses of traditional association (TA) are explored. It is proved that TA is a special case of PA. (2) The algorithms for mining PAR, decoding chromosome and fitness are proposed and implemented. (3) It is also proved that gene decoding procedure always success for any well-defined gene. (4) Extensive experiments are given to demonstrate that PAGEP can discover some association rule that cannot be expressed and discovered by traditional method.
	TO cite this article:Zuo Jie,tangchangjie,zhangtianqing. Mining Predicate Association Rule by Gene Expression Programming [OL].[ 1 July 2004] http://en.paper.edu.cn/en_releasepaper/content/878


	2. Time Series Prediction based on Gene Expression Programming
	Zuo Jie,Tangchangjie,Lichaun,Chen au-long,Yuan Chang-an
	Mechanics 28 June 2004
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	Abstract:Time series prediction is a typical and significant task in data mining, which has been widely studied recently. This paper proposes two novel methods for Time Series Prediction based on GEP (Gene Expression Programming). The main contributions are as follows: (1) GEP-Sliding Window Prediction Method (GEP-SWPM) to mine the relationship between future and history data directly. (2) GEP-Differential Equation Prediction Method (GEP-DEPM) to mine ordinary differential equation from training data, and predicts future trends based on specified initial conditions. (3) A brand new data preprocessing method, called Differential by Microscope Interpolation (DMI) that boosts the effectivity of our methods. (4) A new simple and effective GEP-constants generation method called Meta-Constants (MC) is also proposed. Extensive experiments on real data sets for sun spot prediction show that the performance of the new method is 10-200 times higher than existing algorithms.
	TO cite this article:Zuo Jie,Tangchangjie,Lichaun, et al. Time Series Prediction based on Gene Expression Programming[OL].[28 June 2004] http://en.paper.edu.cn/en_releasepaper/content/867

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	3. Joint Source-Channel Decoding of Huffman Codes with LDPC Codes
	Zhonghui Mei,Lenan Wu
	Mechanics 21 June 2004
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	Abstract:In this paper, we present a joint source-channel decoding algorithm (JSCD) for LDPC codes by exploiting the redundancy of the Huffman coded sources.When the number of Huffman codes increases, just a moderate complexity is added for our algorithm by increasing the size of the lookup table, which is used to estimate the information bit probability based on the source redundancy.
	TO cite this article:Zhonghui Mei,Lenan Wu. Joint Source-Channel Decoding of Huffman Codes with LDPC Codes[OL].[21 June 2004] http://en.paper.edu.cn/en_releasepaper/content/839


	4. Retrospect of Transmission-line Equations Excited by an External Electromagnetic Field
	Xiaohua Wang,Bingzhong Wang
	Mechanics 11 June 2004
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	Abstract:This paper presents the development and existent problems of field-to-transmission line coupling equations. The approaches of different researchers are compared in this paper. And some suggestions for the future research in the area of EMC are given in the last.
	TO cite this article:Xiaohua Wang,Bingzhong Wang. Retrospect of Transmission-line Equations Excited by an External Electromagnetic Field[OL].[11 June 2004] http://en.paper.edu.cn/en_releasepaper/content/815


	5. LDPC Codes for Binary Markov sources
	Zhonghui Mei,Lenan Wu
	Mechanics 07 June 2004
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	Abstract:Abstract In this paper, we present a new joint-source channel decoding algorithm for LDPC codes by exploiting the redundancy of the Markov sources. Simulation results show that when there is more redundancy in the sources, better performance of the decoding algorithm can be obtained.
	TO cite this article:Zhonghui Mei,Lenan Wu. LDPC Codes for Binary Markov sources[OL].[ 7 June 2004] http://en.paper.edu.cn/en_releasepaper/content/794


	6. Joint souce-channel Turbo Coding of Vector Quantized images
	Zhonghui Mei,Lenan Wu
	Mechanics 07 June 2004
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	Abstract:In this paper, we first present an index assignment algorithm which is robust against channel errors. We name this algorithm as chain algorithm. Furthermore, by using this new algorithm to rearrange the order of codevectors in the codebook, the index bits of different position will have different error sensibilities to channel noise, and a good performance of image qualities transimitted over the noisy channel can be obtained after using turbo code to protect some significant bits of the index. Through numerical simulations, the qualities of the image transimitted over noisy channel can be improved without increasing any complexities.
	TO cite this article:Zhonghui Mei,Lenan Wu. Joint souce-channel Turbo Coding of Vector Quantized images[OL].[ 7 June 2004] http://en.paper.edu.cn/en_releasepaper/content/793


	7. Temporal-based Multi-Strokes Sketchy Graphics Recognition
	Yin Jian Feng,Sun Zheng Xing
	Mechanics 20 May 2004
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	Abstract:In this paper, we present a multi-strokes sketchy graphics recognition method, which make user of temporal information of sketching. The main idea includes two aspects: temporal-based stroke segmentation and temporal-based user modeling. The former means that the input stroke is emendated into primitive shapes such as lines, arcs, circles and ellipses, etc., based on pen speed and stroke curvature. The later indicates that a users’ sketching model are constructed and adjusted dynamically based on temporal order of primitive shapes. The experiments have proved the high efficiency.
	TO cite this article:Yin Jian Feng,Sun Zheng Xing. Temporal-based Multi-Strokes Sketchy Graphics Recognition[OL].[20 May 2004] http://en.paper.edu.cn/en_releasepaper/content/706


	8. SPATIAL STYLES CAPTURING USING GENETIC ALGORITHMS
	Zhang Li Sha,Sun Zheng Xing
	Mechanics 20 May 2004
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	Abstract:Many sketches convey information through the relationships between graphics, rather than the internal structure of the graphics themselves, and the spatial context of a sketch should help people to understand freehand sketches. In this paper, we exploit a method of the spatial styles capturing based on genetic algorithms (GAs) for sketch-based software conceptual modeling. Firstly, a structures of object relation graphs(ORGs) is constructed based on the definition of the spatial styles in UML. Secondly, a computational model is employed to describe the interrelation of sketchy UML objects and capture frequently occurred spatial styles possibly with fixed semantics through genetic algorithms. Experiments have proved the proposed method both effective and efficient.
	TO cite this article:Zhang Li Sha,Sun Zheng Xing. SPATIAL STYLES CAPTURING USING GENETIC ALGORITHMS[OL].[20 May 2004] http://en.paper.edu.cn/en_releasepaper/content/702


	9. Sketchy Shape Recognition Based on Relevance Feedback
	Wang Qiang,Sun Zheng Xing
	Mechanics 18 May 2004
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	Abstract:Sketching is an efficient way for recording and conveying ideas, especially in the early stages of design and idea externalizing. Main challenge in this area comes from the ambiguity of sketch. In this paper, we introduce a method of freehand sketchy shape recognition, in which we adapt the relevance feedback for sketchy shapes recognition to capturing user’s intents. Firstly, the candidate sketchy objects are extracted by means of similarity calculation, based on the establishment of feature-based vector model of freehand sketches. Secondly, the relevance feedback are used to capture the users’ input intends and refine the recognition results incrementally by rebuilding the vector and re-weighting the feature-based vector model alone with the user’s relevance judgement. Experiments prove the proposed method both effective and efficient.
	TO cite this article:Wang Qiang,Sun Zheng Xing. Sketchy Shape Recognition Based on Relevance Feedback[OL].[18 May 2004] http://en.paper.edu.cn/en_releasepaper/content/698


	10. INCREMENTAL ON-LINE SKETCHY SHAPE RECOGNITION WITH DYNAMIC MODELING AND RELEVANCE FEEDBACK
	Sun Zheng Xing,Wang Qiang,Yin Jian Feng
	Mechanics 18 May 2004
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	Abstract:In this paper, we have exploited an original principle of the incremental online sketchy shapes recognition. The recognition process is based on two aspects: the dynamic user modeling and relevance feedback. Firstly, we adopt an dynamic user modeling way to build user models for each specific user in an incremental decision tree and recognize dynamically the ‘possible shapes’ by means of fuzzy matching based on the visiting frequency of each recorded shape of user models. Secondly, we make attempt to bring relevance feedback method into the incremental sketchy recognition to to capture users’ input intends and refine the recognition results incrementally. Experiments prove the proposed method both effective and efficient.
	TO cite this article:Sun Zheng Xing,Wang Qiang,Yin Jian Feng. INCREMENTAL ON-LINE SKETCHY SHAPE RECOGNITION WITH DYNAMIC MODELING AND RELEVANCE FEEDBACK[OL].[18 May 2004] http://en.paper.edu.cn/en_releasepaper/content/696

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