Browsing by Author "Cheng, CS"

Now showing 1 - 6 of 6

A unique-order interpolative code for fast querying and space-efficient indexing in information retrieval systems
(2004)
Cheng, CS
;
Shann, JJJ
;
Chung, CP
;
資訊工程學系
;
Department of Computer Science
The word positions for any given word in the whole collection are arranged in clusters. If we can use the method that can take advantage of clustering, excellent results can be achieved in compression of inverted file. However, the mechanisms of decoding in all the well-known compression methods that can exploit clustering are more complex, which reduce the ability of searching performance in information retrieval system (IRS) at some degree. In this paper, we proposed a new method that can facilitate coding and decoding of interpolative code by using the simply applied and high-speed models such as gamma code and Golomb code in d-gap technique. This new method can exploit clustering well, and the experimental results confirm that our method can provide fast decoding speed and excellent compression efficiency.
Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)
(2003)
Wang, CH
;
Cheng, CS
;
Lee, TT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Type-2 fuzzy logic system (FLS) cascaded with neural network called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural network as the consequent part. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process. It can also be shown both learning rates can not be both negative. Excellent results are obtained for the truck backing-up control, which yield more improved performance than those using type-1 FNN.
Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)
(2004-06-01)
Wang, CH
;
Cheng, CS
;
Lee, TT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Type-2 fuzzy logic system (FLS) cascaded with neural network, type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part, and the two-layer interval neural network as the consequent part. A general T2FNN is computational-intensive due to the complexity of type 2 to type 1 reduction. Therefore, the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates cannot be both negative. Further, due to variation of the initial MF parameters, i.e., the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search optimal spread rate for uncertain means and optimal learning for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.
Fast query evaluation through document identifier assignment for inverted file-based information retrieval systems
(2006-05-01)
Cheng, CS
;
Chung, CP
;
Shann, JJJ
;
資訊工程學系
;
Department of Computer Science
Compressing an inverted file can greatly improve query performance of an information retrieval system (IRS) by reducing disk I/Os. We observe that a good document identifier assignment (DIA) can make the document identifiers in the posting lists more clustered, and result in better compression as well as shorter query processing time. In this paper, we tackle the NP-complete problem of finding an optimal DIA to minimize the average query processing time in an IRS when the probability distribution of query terms is given. We indicate that the greedy nearest neighbor (Greedy-NN) algorithm can provide excellent performance for this problem. However, the Greedy-NN algorithm is inappropriate if used in large-scale IRSs, due to its high complexity O(N-2 x n), where N denotes the number of documents and n denotes the number of distinct terms. In real-world IRSs, the distribution of query terms is skewed. Based on this fact, we propose a fast O(N x n) heuristic, called partition-based document identifier assignment (PBDIA) algorithm, which can efficiently assign consecutive document identifiers to those documents containing frequently used query terms, and improve compression efficiency of the posting lists for those terms. This can result in reduced query processing time. The experimental results show that the PBDIA algorithm can yield a competitive performance versus the Greedy-NN for the DIA problem, and that this optimization problem has significant advantages for both long queries and parallel information retrieval (IR). (c) 2005 Elsevier Ltd. All rights reserved.
Orientation-enhanced growth and optical properties of ZnO nanowires grown on porous silicon substrates
(2005-02-01)
Hsu, HC
;
Cheng, CS
;
Chang, CC
;
Yang, S
;
Chang, CS
ZnO nanowires have been synthesized on porous silicon substrates with different porosities via the vapour-liquid-solid method. The texture coefficient analysed from the XRD spectra indicates that the nanowires are more highly orientated on the appropriate porosity of porous silicon substrate than on the smooth surface of silicon. The Raman spectrum reveals the high quality of the ZnO nanowires. From the temperature-dependent photoluminescence spectra, we deduced the activation energies of free and bound excitons.
Unique-order interpolative coding for fast querying and space-efficient indexing in information retrieval systems
(2006-03-01)
Cheng, CS
;
Shann, JJJ
;
Chung, CP
;
資訊工程學系
;
Department of Computer Science
This paper presents a size reduction method for the inverted file, the most suitable indexing structure for an information retrieval system (IRS). We notice that in an inverted file the document identifiers for a given word are usually clustered. While this Clustering property can be used in reducing the size of the inverted file, good compression as well as fast decompression must both be available. In this paper, we present it method that can facilitate coding and decoding processes for interpolative coding using recursion elimination and loop unwinding. We call this method the unique-order interpolative coding. It can calculate the lower and upper bounds of every document identifier for a binary code without using a recursive process, hence the decompression time can be greatly reduced. Moreover, it also can exploit document identifier Clustering to compress the inverted file efficiently. Compared with the other well-known compression methods, our method provides fast decoding speed and excellent compression. This method can also be used to support a self-indexing strategy. Therefore our research work in this paper provides a feasible way to build a fast and space-economical IRS. (c) 2005 Elsevier Ltd. All rights reserved.