• Communications for Statistical Applications and Methods
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• v.29 no.6
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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.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.95-110
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• 1996

We consider the question of efficiency of the Bayes sequential procedure with respect to the optimal fixed sample size Bayes procedure in a simple vs. simple testing problem for data coming from a general nonregular density b(.theta.)h(x)l(x < .theta.). Efficiency is defined in two different ways in these caiculations. Also, the minimax sequential risk (and minimax sequential stratage) is studied as a function of the cost of sampling.

• Journal of information and communication convergence engineering
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• v.8 no.4
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• pp.421-426
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• 2010

This paper presents a method of design the counter using sequential system based on synchronous techniques. For the design the counter, first of all, we derive switching algebras and their operations. Also, we obtain the next-state functions, flip-flop excitations and their input functions from the flip-flop. Then, we propose the algorithm which is a method of implementation of the synchronous sequential digital logic circuits. Finally, we apply proposed the sequential logic based on synchronous techniques to counter.

• International Journal of Reliability and Applications
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• v.11 no.1
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• pp.17-22
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• 2010

It is desired to estimate the reliability of a system that has two subsystems connected in series where each subsystem has two components connected in parallel. A batch sequential sampling scheme is introduced. It is shown that the batch sequential sampling scheme is asymptotically optimal as the total number of units goes to infinity. Numerical comparisons indicate that the batch sequential sampling scheme performs better than the balanced sampling scheme and is nearly optimal.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.207-220
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• 1999

In this paper, some of the issue about a group sequential method are considered in the Bayesian context. The continuous time optimal stopping boundary can be used to approximate the optimal stopping boundary for group sequential designs. The exact stopping boundary for group sequential design is obtained by using the backward induction method and is compared with the continuous optimal stopping boundary and the corrected continuous stopping boundary.

• Proceedings of the KSRS Conference
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• v.2
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• pp.627-630
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• 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.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.457-462
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.8
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• pp.467-473
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• 2014

In the direct sequence spread spectrum system, the correct synchronization is very important; hence, several acquisition schemes based on the sequential estimation have been developed. Typically, the rapid acquisition sequential estimation (RASE) scheme, the seed accumulating sequential estimation (SASE) scheme, the recursive soft sequential estimation (RSSE) scheme have been developed for the correct acquisition. However, the objective performance comparison and analysis between former estimation schemes have not been performed so far. In this paper, we compare and analyze the performance of the above sequential estimation schemes by simulating the correct chip probability and the mean acquisition time (MAT).

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.55-75
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• 2010

Sequential pattern mining aims to discover interesting sequential patterns in a sequence database, and it is one of the essential data mining tasks widely used in various application fields such as Web access pattern analysis, customer purchase pattern analysis, and DNA sequence analysis. In general sequential pattern mining, only the generation order of data element in a sequence is considered, so that it can easily find simple sequential patterns, but has a limit to find more interesting sequential patterns being widely used in real world applications. One of the essential research topics to compensate the limit is a topic of weighted sequential pattern mining. In weighted sequential pattern mining, not only the generation order of data element but also its weight is considered to get more interesting sequential patterns. In recent, data has been increasingly taking the form of continuous data streams rather than finite stored data sets in various application fields, the database research community has begun focusing its attention on processing over data streams. The data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. In data stream processing, each data element should be examined at most once to analyze the data stream, and the memory usage for data stream analysis should be restricted finitely although new data elements are continuously generated in a data stream. Moreover, newly generated data elements should be processed as fast as possible to produce the up-to-date analysis result of a data stream, so that it can be instantly utilized upon request. To satisfy these requirements, data stream processing sacrifices the correctness of its analysis result by allowing some error. Considering the changes in the form of data generated in real world application fields, many researches have been actively performed to find various kinds of knowledge embedded in data streams. They mainly focus on efficient mining of frequent itemsets and sequential patterns over data streams, which have been proven to be useful in conventional data mining for a finite data set. In addition, mining algorithms have also been proposed to efficiently reflect the changes of data streams over time into their mining results. However, they have been targeting on finding naively interesting patterns such as frequent patterns and simple sequential patterns, which are found intuitively, taking no interest in mining novel interesting patterns that express the characteristics of target data streams better. Therefore, it can be a valuable research topic in the field of mining data streams to define novel interesting patterns and develop a mining method finding the novel patterns, which will be effectively used to analyze recent data streams. This paper proposes a gap-based weighting approach for a sequential pattern and amining method of weighted sequential patterns over sequence data streams via the weighting approach. A gap-based weight of a sequential pattern can be computed from the gaps of data elements in the sequential pattern without any pre-defined weight information. That is, in the approach, the gaps of data elements in each sequential pattern as well as their generation orders are used to get the weight of the sequential pattern, therefore it can help to get more interesting and useful sequential patterns. Recently most of computer application fields generate data as a form of data streams rather than a finite data set. Considering the change of data, the proposed method is mainly focus on sequence data streams.