• Proceedings of the KOR-KST Conference
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• 2000.02a
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• pp.25-47
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• 2000

In route guidance systems fastest-path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this study is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e. Pareto optimal) paths for real-time vehicle routing which reflect the drivers' preferences and route choice behaviors. We propose two pruning algorithms that reduce the search area based on a context-dependent linear utility function and thus reduce the computation time. The basic notion of the proposed approach is that ⅰ) enumerating all nondominated paths is computationally too expensive, ⅱ) obtaining a stable mathematical representation of the drivers' utility function is theoretically difficult and impractical, and ⅲ) obtaining optimal path given a nonlinear utility function is a NP-hard problem. Consequently, a heuristic two-stage strategy which identifies multiple routes and then select the near-optimal path may be effective and practical. As the first stage, we utilize the relaxation based pruning technique based on an entropy model to recognize and discard most of the nondominated paths that do not reflect the drivers' preference and/or the context-dependency of the preference. In addition, to make sure that paths identified are dissimilar in terms of links used, the number of shared links between routes is limited. We test the proposed algorithms in a large real-life traffic network and show that the algorithms reduce CPU time significantly compared with conventional multi-criteria shortest path algorithms while the attributes of the routes identified reflect drivers' preferences and generic route choice behaviors well.

• Journal of Communications and Networks
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• v.18 no.1
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• pp.27-39
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• 2016

This paper derives and analyzes a novel block fast Fourier transform (FFT) based joint detection algorithm. The paper compares the performance and complexity of the novel block-FFT based joint detector to that of the Cholesky based joint detector and single user detection algorithms. The novel algorithm can operate at chip rate sampling, as well as higher sampling rates. For the performance/complexity analysis, the time division duplex (TDD) mode of a wideband code division multiplex access (WCDMA) is considered. The results indicate that the performance of the fast FFT based joint detector is comparable to that of the Cholesky based joint detector, and much superior to that of single user detection algorithms. On the other hand, the complexity of the fast FFT based joint detector is significantly lower than that of the Cholesky based joint detector and less than that of the single user detection algorithms. For the Cholesky based joint detector, the approximate Cholesky decomposition is applied. Moreover, the novel method can also be applied to any generic multiple-input-multiple-output (MIMO) system.

• The Transactions of the Korea Information Processing Society
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• v.5 no.6
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• pp.1633-1638
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• 1998

In this paper we propose a generic BIST builder for the Embedded Multiple HAM modules in ASICs, The BlST circuitry is automatically generated according to the specification of the target RAM Modules and the applying test algorithms to them. The lJIST is designed using the TOP-DOWN technique and, thus, has the several advantages in the area of the selection of test algorithm, the development of the circuitry, and the reuse of the circuitry, In addition, we have modified the existing serial interiacing approach to obtain smaller additional BlST circuitry and higher fault coverage and better B1ST sharing of the target RAM Modules in ASICs.

• Smart Structures and Systems
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• v.21 no.3
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• pp.373-387
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• 2018

Semi-active isolation systems based on leverage-type stiffness control strategies have been widely studied. The main concept behind this type of system is to adjust the stiffness in the isolator to match the fundamental period of the isolated system by using a simple leverage mechanism. Although this system achieves high performance under far-field earthquakes, it is unsuitable for near-fault strong ground motion. To overcome this problem, this study considers the potential energy effect in the control law of the semi-active isolation system. The minimal energy weighting (MEW) between the potential energy and kinetic energy was first optimized through a series of numerical simulations. Two MEW algorithms, namely generic and near-fault MEW control, were then developed to efficiently reduce the structural displacement responses. To demonstrate the performance of the proposed method, a two-degree-of-freedom structure was employed as a benchmark. Numerical results indicate that the dynamic response of the structure can be effectively dampened by the proposed MEW control under both far-field and near-fault earthquakes, whereas the structural responses resulting from conventional control methods may be greater than those for the purely passive control method. Moreover, according to experimental verifications, both the generic and near-fault MEW control modes yielded promising results under impulse-like earthquakes. The practicability of the proposed control algorithm was verified.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.247-258
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• 2003

Principal component analysis (PCA) based algorithms form the basis of numerous algorithms and studies in the face recognition literature. PCA is a statistical technique and its incorporation into a face recognition system requires numerous design decisions. We explicitly take the design decisions by in-troducing a generic modular PCA-algorithm since some of these decision ate not documented in the literature We experiment with different implementations of each module, and evaluate the different im-plementations using the September 1996 FERET evaluation protocol (the do facto standard method for evaluating face recognition algorithms). We experiment with (1) changing the illumination normalization procedure; (2) studying effects on algorithm performance of compressing images using JPEG and wavelet compression algorithms; (3) varying the number of eigenvectors in the representation; and (4) changing the similarity measure in classification process. We perform two experiments. In the first experiment, we report performance results on the standard September 1996 FERET large gallery image sets. The result shows that empirical analysis of preprocessing, feature extraction, and matching performance is extremely important in order to produce optimized performance. In the second experiment, we examine variations in algorithm performance based on 100 randomly generated image sets (galleries) of the same size. The result shows that a reasonable threshold for measuring significant difference in performance for the classifiers is 0.10.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.3-5
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• 2005

In this paper. we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C-Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• International Journal of CAD/CAM
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• v.6 no.1
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• pp.105-115
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• 2006

Thickness is a commonly used parameter in product design and manufacture. Its intuitive definition as the smallest dimension of a cross-section or the minimum distance between two opposite surfaces is ambiguous for intricate solids, and there is very little reported work in automatic computation of thickness. We present three generic definitions of thickness: interior thickness of points inside an object, exterior thickness for points on the object surface, and radiographic thickness along a view direction. Methods for computing and displaying the respective thickness values are also presented. The internal thickness distribution is obtained by peeling or successive skin removal, eventually revealing the object skeleton (similar to medial axis transformation). Another method involves radiographic scanning along a viewing direction, with minimum, maximum and total thickness options, displayed on the surface of the object. The algorithms have been implemented using an efficient voxel based representation that can handle up to one billion voxels (1000 per axis), coupled with a near-real time display scheme that uses a look-up table based on voxel neighborhood configurations. Three different types of intricate objects: industrial (press cylinder casting), sculpture (Ganesha idol), and medical (pelvic bone) were used for successfully testing the algorithms. The results are found to be useful for early evaluation of manufacturability and other lifecycle considerations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2680-2700
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• 2017

Finite field arithmetic over GF($2^m$) is used in a variety of applications such as cryptography, coding theory, computer algebra. It is mainly used in various cryptographic algorithms such as the Elliptic Curve Cryptography (ECC), Advanced Encryption Standard (AES), Twofish etc. The multiplication in a finite field is considered as highly complex and resource consuming operation in such applications. Many algorithms and architectures are proposed in the literature to obtain efficient multiplication operation in both hardware and software. In this paper, a modified serial multiplication algorithm with interleaved modular reduction is proposed, which allows for an efficient realization of a sequential polynomial basis multiplier. The proposed sequential multiplier supports multiplication of any two arbitrary finite field elements over GF($2^m$) for generic irreducible polynomials, therefore made versatile. Estimation of area and time complexities of the proposed sequential multiplier is performed and comparison with existing sequential multipliers is presented. The proposed sequential multiplier achieves 50% reduction in area-delay product over the best of existing sequential multipliers for m = 163, indicating an efficient design in terms of both area and delay. The Application Specific Integrated Circuit (ASIC) and the Field Programmable Gate Array (FPGA) implementation results indicate a significantly less power-delay and area-delay products of the proposed sequential multiplier over existing multipliers.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.161-170
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• 2007

The proposed switch which has separated data plane and switching plane can make parallel processing for packet data storing, memory address pointer switching and simultaneously can be capable of switching the variable length for IP packets. The proposed architecture does not require the complicated arbitration algorithms in VOQ, also is designed for QoS of generic output queue switch as well as input queue. At the result of simulations, the proposed architecture has less average packet delay than the one of the memory-sharing based architecture and guarantees keeping a certain average packet delay in increasing switch size.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.580-582
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• 2005

This paper presents a new method which applies a genetic algorithm(GA) for determining which sectionalizing switch to operate in order to solve the distribution system loss minimization re-configuration problem. The distribution system loss minimization re-configuration problem is in essence a 0-1 planning problem which means that for typical system scales the number of combinations requiring searches becomes extremely large. In order to deal with this problem, a new a roach which applies a GA was presented. Briefly, GA are a type of random number search method, however, they incorporate a multi-point search feature. Further, every point is not is not separately and respectively renewed, therefore, if parallel processing is applied, we can expect a fast solution algorithm to result.