• Proceedings of the IEEK Conference
• /
• 2005.11a
• /
• pp.19-22
• /
• 2005

In order to achieve high capacity and reliable link quality in user communication, this paper proposes a new concept of virtual cell: the Dual Virtual Cell(DVC), and DVC employment strategy based on DWCS. The proposed system uses two kinds of virtual cell. One is the AVC(Active Virtual Cell) which exists for actual traffic and the other is the CVC(Candidate Virtual Cell) which contains a set of candidate antennas to protect user's link quality from performance degradation or interruption. And also this system aims to reduce MT's overloads and acheive a prompt link change successfuly by introducing DVC structure which makes it possible for network to monitor real-time channel and to conrol communication links. The proposed system constructs DVC by using antenna selection method and improves frame error performance with employing Space-Time Trellis Code(STTC).

• Korean Management Science Review
• /
• v.17 no.2
• /
• pp.67-73
• /
• 2000

This paper describes a scheduling support system for a plant where the machine environment may be modeled as non-identical parallel machine lines (NPML). That is, there are a number of stages in series with various different-capability-machines at each stage. Arriving continuously are jobs with their specific due dates, processing times and candidate processing machines. We’ve developed a real-time scheduling module in conjunction with a supporting production information system which supplies necessary data to the module. This scheduling module is one of the 9 modules in this system, and is composed of both a scheduling interface and a production monitoring interface. This module allows users to generate many candidate schedules by selecting their business policies. The selective arguments which are available consist of allocation costs, batch sizes and machine selection intervals. They are now being implemented at a powder metallurgy plant.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.26 no.9
• /
• pp.1395-1401
• /
• 1989

An efficient file allocation algorithm and a new method which calculate appraisal value of candidate computer site for distributed computer systems are proposed. The file allocation problem size is reduced by using the preassignment condition. The appraisal value of candidate node is calcualted as the user state array and node state array are varied according to control variables. As the selection criteria is applied to the candidates, the reasonable node is selected and assign state is determined. The proposed algorithm is heuriatic polynomial time algorithm. By performing algorithm for sample problems. It is shown that the proposed algorithm is superior to conventional method in terms of deviation from optimal solution.

• Proceedings of the KSPS conference
• /
• 2006.11a
• /
• pp.142-145
• /
• 2006

Discrimination between speech and music is important in many multimedia applications. Previously we proposed a new parameter for speech/music discrimination, the mean of minimum cepstral distances (MMCD), and it outperformed the conventional parameters. One weakness of it is that its performance depends on range of candidate frames to compute the minimum cepstral distance, which requires the optimal selection of the range experimentally. In this paper, to alleviate the problem, we propose a multi-dimensional MMCD parameter which consists of multiple MMCDs with different ranges of candidate frames. Experimental results show that the multi-dimensional MMCD parameter yields an error rate reduction of 22.5% compared with the optimally chosen one-dimensional MMCD parameter.

• Phonetics and Speech Sciences
• /
• v.1 no.3
• /
• pp.109-115
• /
• 2009

Many isolated word recognition systems may generate non-similar words for recognition candidates because they use only acoustic information. In this paper, we investigate several techniques which can exclude non-similar words from N-best candidate words by applying Levenstein distance measure. At first, word distance method based on phone and syllable distances are considered. These methods use just Levenstein distance on phones or double Levenstein distance algorithm on syllables of candidates. Next, word similarity approaches are presented that they use characters' position information of word candidates. Each character's position is labeled to inserted, deleted, and correct position after alignment between source and target string. The word similarities are obtained from characters' positional probabilities which mean the frequency ratio of the same characters' observations on the position. From experimental results, we can find that the proposed methods are effective for removing non-similar words without loss of system performance from the N-best recognition candidates of the systems.

• Phonetics and Speech Sciences
• /
• v.2 no.4
• /
• pp.153-161
• /
• 2010

Many isolated word recognition systems may generate non-similar words for recognition candidates because they use only acoustic information. The previous study [1,2] investigated several techniques which can exclude non-similar words from N-best candidate words by applying Levenstein distance measure. This paper discusses the various improving techniques of removing non-similar recognition results. The mentioned methods include comparison penalties or weights, phone accuracy based on confusion information, weights candidates by ranking order and partial comparisons. Through experimental results, it is found that some proposed method keeps more accurate recognition results than the previous method's results.

• Communications for Statistical Applications and Methods
• /
• v.22 no.5
• /
• pp.507-518
• /
• 2015

It is not easy to select a linear mixed model since the main interest for model building could be different and the number of parameters in the model could not be clearly defined. In this paper, performance of conditional Akaike Information Criteria and its bias-corrected version are compared with marginal Bayesian and Akaike Information Criteria through a simulation study. The results from the simulation study indicate that bias-corrected conditional Akaike Information Criteria shows promising performance when candidate models exclude large models containing the true model, but bias-corrected one prefers over-parametrized models more intensively when a set of candidate models increases. Marginal Bayesian and Akaike Information Criteria also have some difficulty to select the true model when the design for random effects is nested.

• Proceedings of the Korea Society of Poultry Science Conference
• /
• 2003.11a
• /
• pp.129-130
• /
• 2003

There is growing interest for improving meat quality in chicken. Recently, the proteomics can be used as a valuable tool for identifying candidate proteins. In this study, we investigated the proteins expressed in chicken muscle for obtaining chicken muscle reference two dimensional(2D) map and identifying the proteins in muscle affecting Ginseng diet. A few candidate proteins have been currently characterizing using MALDI-TOF Mass spectrometry. Further investigations of the proteins can be used as valuable markers for selection of better quality chicken meat.

• MALSORI
• /
• no.60
• /
• pp.191-201
• /
• 2006

Discrimination between speech and music is important in many multimedia applications. Previously we proposed a new parameter for speech/music discrimination, the mean of minimum cepstral distances (MMCD), and it outperformed the conventional parameters. One weakness of MMCD is that its performance depends on range of candidate frames to compute the minimum cepstral distance, which requires the optimal selection of the range experimentally. In this paper, to alleviate the problem, we propose a multi-dimensional MMCD parameter which consists of multiple MMCDS with combination of different candidate frame ranges. Experimental results show that the multi-dimensional MMCD parameter yields an error rate reduction of 22.5% compared with the optimally chosen one-dimensional MMCD parameter.

• The Journal of the Korea Contents Association
• /
• v.10 no.5
• /
• pp.1-9
• /
• 2010

This paper propose a novel scheme called selective evaluation to improve convergence of ACO (ant colony optimization) for feature selection. The scheme cutdown the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. With the aim of checking applicability of algorithms according to problem size, we analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.