• Journal of Power Electronics
• /
• 제5권2호
• /
• pp.142-150
• /
• 2005

This paper presents a systematic approach to study the carrier based pulse width modulation (PWM) techniques applied to diode-clamped and cascade multilevel inverters by using multi-modulating patterns. This method is based on the description of controllable redundant parameters in the modulating signals. A unified mathematical formulation is presented for carrier based PWM methods, which obtains outputs similar to the corresponding space vector PWM. A full and separate control of the fundamental voltage, vector redundancies and phase redundancies can be obtained in the carrier based PWM. In this paper, the proposed PWM method and corresponding algorithm for generating multi-modulating signals will be formulated and demonstrated by our simulations.

• 제어로봇시스템학회논문지
• /
• 제17권11호
• /
• pp.1132-1137
• /
• 2011

Critical systems with high reliability feature fault tolerant redundancy. Conventional analytical reliability analysis methods that use the Reliability Block Diagram do not adequately reflect characteristics of the redundancy management system and are not suitable for this applications. This paper uses Monte-Carlo method to calculate the reliability of complicated redundant systems. The method was first validated for cases with analytical solutions. Then, the tool was successfully applied to analyze reliability of the flight control systems with a voter as redundancy management system.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.2029-2032
• /
• 2004

The quantity of data were rapidly increased recently and caused the data overwhelming. This led to be difficult in searching the required data. The method of eliminating redundant data was needed. One of the efficient methods was Knowledge Discovery in Database (KDD). Generally data can be separate into 2 cases, continuous data and discrete data. This paper describes algorithm that transforms continuous attributes into discrete ones. We present an Improved Class Attribute Interdependence Maximization (ICAIM), which designed to work with supervised data, for discretized process. The algorithm does not require user to predefine the number of intervals. ICAIM improved CAIM by using significant test to determine which interval should be merged to one interval. Our goal is to generate a minimal number of discrete intervals and improve accuracy for classified class. We used iris plant dataset (IRIS) to test this algorithm compare with CAIM algorithm.

• Journal of Computing Science and Engineering
• /
• 제3권2호
• /
• pp.90-108
• /
• 2009

In this paper, we present an ontology mediation solution based on the methods frequently used in Formal Concept Analysis. Our approach of mediation is based on the existence of instances associated to two source ontologies, then we can generate concepts in a new ontology if and only if they share the same extent. Hence our approach creates a merged ontology which captures the knowledge of these two source ontologies. The main contributions of this work are (i) to enable the creation of concepts not originally in the source ontologies, (ii) to propose a solution to label these emerging concepts and finally (iii) to optimize the resulting ontology by eliminating redundant or non pertinent concepts. Another contribution of this work is to emphasize that several forms of mediated ontology can be defined based on the relaxation of certain criteria produced from our method. The solution that we propose for tackling these issues is an automatic solution, meaning that it does not require the intervention of the end-user, excepting for the definition of the common set of ontology instances.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제13권1호
• /
• pp.73-82
• /
• 2013

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

• 한국안전학회지
• /
• 제14권2호
• /
• pp.155-162
• /
• 1999

reliability from components reliability. In this case, it assumes that components failure is mutually independent, but it may not true in real systems. In this study, the mean cost per unit time is computed as the ratio of mean life to the mean cost. The mean life is obtained by the reliability function under power rule model. The mean cost is obtained by the mathematical model based on the inspection interval. A heuristic method is proposed to determine the optimal number of redundant units and the optimal inspection interval to minimize the mean cost per unit time. The assumptions of this study are as following : First, in the load-sharing k-out-of-n:G system, total loads are applied to the system and shared by the operating components. Secondly, the number of failed components affects the failure rate of surviving components as a function of the total load applied. Finally, the relation between the load and the failure rate of surviving components is set by the power rule model. For the practical application of the above methods, numerical examples are presented.

• ETRI Journal
• /
• 제32권5호
• /
• pp.795-800
• /
• 2010

Embedding a large vocabulary speech recognition system in mobile devices requires a reduced acoustic model obtained by eliminating redundant model parameters. In conventional optimization methods based on the minimum description length (MDL) criterion, a binary Gaussian tree is built at each state of a hidden Markov model by iteratively finding and merging similar mixture components. An optimal subset of the tree nodes is then selected to generate a downsized acoustic model. To obtain a better binary Gaussian tree by improving the process of finding the most similar Gaussian components, this paper proposes a new distance measure that exploits the difference in likelihood values for cases before and after two components are combined. The mixture weight of Gaussian components is also introduced in the component merging step. Experimental results show that the proposed method outperforms MDL-based optimization using either a Kullback-Leibler (KL) divergence or weighted KL divergence measure. The proposed method could also reduce the acoustic model size by 50% with less than a 1.5% increase in error rate compared to a baseline system.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
• /
• pp.467-469
• /
• 2006

In wireless network environments, as users' demands on high-speed data communications due to increase of multi-media services, the necessity of new high-speed WLAN technologies has appeared. Nowaday, IEEE is standardizing a new WLAN protocol caned as IEEE 802.11n. To effectively use wireless resources, IEEE 802.11n introduces MAC aggregation function which is similar to that in IEEE 802.11e. In case of transmitting several frames without MAC aggregation, the frames include individual frame header and trailer, and their corresponding acknowledgement frames can appear on wireless link. However, if they are aggregated into single MAC frame, we can reduce the number of used bits due to frame headers/trailers and also remove redundant acknowledgement frames. In this paper, we explain two different MAC frame aggregation methods for IEEE 802.11e and IEEE 802.11n and evaluate their throughput by simulations.

• Communications for Statistical Applications and Methods
• /
• 제17권1호
• /
• pp.17-27
• /
• 2010

분류분석에 사용되는 k-최근접이웃 분류기에 유전알고리즘을 적용하여 의미 있는 변수들과 이들에 대한 가중치 그리고 적절한 k를 동시에 선택하는 알고리즘을 제시하였다. 다양한 실제 자료에 대하여 기존의 여러 방법들과 교차타당성 방법을 통하여 비교한 결과 효과적인 것으로 나타났다.

• The Journal of the Acoustical Society of Korea
• /
• 제20권4E호
• /
• pp.31-38
• /
• 2001

A large-scale synthesis database for a unit selection based synthesis method usually retains redundant synthesis unit instances, which are useless to the synthetic speech quality. In this paper, to eliminate those instances from the synthesis database, we proposed a new pruning method called weighted vector quantization (WVQ). The WVQ reflects relative importance of each synthesis unit instance when clustering the similar instances using vector quantization (VQ) technique. The proposed method was compared with two conventional pruning methods through the objective and subjective evaluations of the synthetic speech quality: one to simply limit maximum number of instance, and the other based on normal VQ-based clustering. The proposed method showed the best performance under 50% reduction rates. Over 50% of reduction rates, the synthetic speech quality is not seriously but perceptibly degraded. Using the proposed method, the synthesis database can be efficiently reduced without serious degradation of the synthetic speech quality.