• Communications for Statistical Applications and Methods
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• 제29권6호
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• pp.721-733
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• 2022

In this paper we compare parameter estimation by Grassmann manifold optimization and sequential candidate set algorithm in a structured principal fitted component (PFC) model. The structured PFC model extends the form of the covariance matrix of a random error to relieve the limits that occur due to too simple form of the matrix. However, unlike other PFC models, structured PFC model does not have a closed form for parameter estimation in dimension reduction which signals the need of numerical computation. The numerical computation can be done through Grassmann manifold optimization and sequential candidate set algorithm. We conducted numerical studies to compare the two methods by computing the results of sequential dimension testing and trace correlation values where we can compare the performance in determining dimension and estimating the basis. We could conclude that Grassmann manifold optimization outperforms sequential candidate set algorithm in dimension determination, while sequential candidate set algorithm is better in basis estimation when conducting dimension reduction. We also applied the methods in real data which derived the same result.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.858-863
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• 2004

In the optimized design of an actual structure, the design variable should be selected among any certain values or corresponds to a discrete design variable that needs to handle the size of a pre-formatted part. Various algorithms have been developed for discrete design. As recently reported, the sequential algorithm with orthogonal arrays(SOA), which is a local minimum search algorithm in discrete space, has excellent local minimum search ability. It reduces the number of function evaluation using orthogonal arrays. However it only finds a local minimum and the final solution depends on the initial value. In this research, the genetic algorithm, which defines an initial population with the potential solution in a global space, is adopted in SOA. The new algorithm, sequential algorithm with orthogonal arrays and genetic algorithm(SOAGA), can find a global solution with the properties of genetic algorithm and the solution is found rapidly with the characteristics of SOA.

• ETRI Journal
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• 제29권3호
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• pp.336-352
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• 2007

Sequential pattern mining has become an essential task with broad applications. Most sequential pattern mining algorithms use a minimum support threshold to prune the combinatorial search space. This strategy provides basic pruning; however, it cannot mine correlated sequential patterns with similar support and/or weight levels. If the minimum support is low, many spurious patterns having items with different support levels are found; if the minimum support is high, meaningful sequential patterns with low support levels may be missed. We present a new algorithm, weighted interesting sequential (WIS) pattern mining based on a pattern growth method in which new measures, sequential s-confidence and w-confidence, are suggested. Using these measures, weighted interesting sequential patterns with similar levels of support and/or weight are mined. The WIS algorithm gives a balance between the measures of support and weight, and considers correlation between items within sequential patterns. A performance analysis shows that WIS is efficient and scalable in weighted sequential pattern mining.

• 제어로봇시스템학회논문지
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• 제11권7호
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• pp.627-632
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• 2005

RAIM techniques based on TLS have rarely been addressed because TLS requires a great number of computations. In this paper, the particular form of the observation matrix H, is exploited so as to develop a new TLS-based sequential algorithm to identify an errant satellite. The algorithm allows us to enjoy the advantages of TLS with less computational burden. The proposed algorithm is verified through a numerical simulation.

• International Journal of Control, Automation, and Systems
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• 제2권2호
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• pp.165-170
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• 2004

RAIM techniques based on TLS have rarely been addressed because TLS requires a great number of computations. In this paper, the particular form of the observation matrix H, is exploited so as to develop a new TLS-based sequential algorithm to identify an errant satellite. The algorithm allows us to enjoy the advantages of TLS with less computational burden. The proposed algorithm is verified through a numerical simulation.

• 대한건축학회논문집:구조계
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• 제36권5호
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• pp.187-194
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• 2020

This study presents the energy-saving sequential control algorithm to handle indoor CO₂ and PM2.5 for the improvement of the air quality of school classrooms. To solve indoor air quality (IAQ) problems, air cleaning and ventilation systems are mainly used for school classrooms. Although air cleaning is able to collect PM2.5, it is difficult to remove harmful gas substances. The ventilation system is suitable to tackle CO and CO₂, the volume ventilation, however, is relatively small. In this paper, to remove CO₂ and PM2.5, the pollutant balance equation for improving indoor air quality is reviewed. The sequential control algorithm of the ventilation and air cleaning system with four levels of criteria is introduced for the effective removal of pollutants. The proposed sequential control algorithm confirms that indoor CO₂ and PM2.5 can be properly controlled below the standard value. In addition, the sequential operation of air cleaning and ventilation systems has shown significant improvement in IAQ compared to the independent ventilation system operation. Particularly, such systems are efficient when outdoor PM2.5 is high.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.116-122
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• 2023

Sentiment categorization technique be commonly isolated interested in threes significant classifications name Machine Learning Procedure (ML), Lexicon Based Method (LB) also finally, the Hybrid Method. In Machine Learning Methods (ML) utilizes phonetic highlights with apply notable ML algorithm. In this paper, in classification and identification be complete base under in optimizations technique called sequential minimal optimization with Random Forest algorithm (SMORF) for expanding the exhibition and proficiency of sentiment classification framework. The three existing classification algorithms are compared with proposed SMORF algorithm. Imitation result within experiential structure is Precisions (P), recalls (R), F-measures (F) and accuracy metric. The proposed sequential minimal optimization with Random Forest (SMORF) provides the great accuracy.

• 대한전기학회논문지
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• 제33권2호
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• pp.47-55
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• 1984

This paper is concerned with a computer-aided state assignment, that is, coding race-free internal states of asynchronous sequential circuits, and a method for minimizing the combinational network of asynchronous sequential circuits. The FORTRAN version of the peoposed algorithm results in race-free state assignments and reduction of the number of connections and gates with near minimal hardware cost. Some examples are designed by the proposed computer program to illustrate the algorithm in this paper. Finally, results are compared with those of the other methods.

• 대한기계학회논문집A
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• 제29권10호
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• pp.1369-1376
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• 2005

In structural design, the design variables are frequently selected from certain discrete values. Various optimization algorithms have been developed fDr discrete design. It is well known that many function evaluations are needed in such optimization. Recently, sequential algorithm with orthogonal arrays (SOA), which is a search algorithm for a local minimum in a discrete space, has been developed. It considerably reduces the number of function evaluations. However, it only finds a local minimum and the final solution depends on the initial values of the design variables. A new algorithm is proposed to adopt a genetic algorithm (GA) in SOA. The GA can find a solution in a global sense. The solution from the GA is used as the initial design of SOA. A sequential usage of the GA and SOA is carried out in an iterative manner until the convergence criteria are satisfied. The performance of the algorithm is evaluated by various examples.

• International Journal of Contents
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• 제3권2호
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• pp.18-24
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• 2007

Biological sequences such as DNA and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of more than hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological datasets with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with a fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. The experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.