• Journal of the Korea Society of Computer and Information
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• v.22 no.2
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• pp.81-87
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• 2017

Nowadays wine is increasingly enjoyed by a wider range of consumers, and wine certification and quality assessment are key elements in supporting the wine industry to develop new technologies for both wine making and selling processes. There have been many attempts to construct a more methodical approach to the assessment of wines, but most of them rely on objective decision rather than subjective judgement. In this paper, we propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. We used sequential forward selection and decision tree for this purpose. Experiments with the wine quality dataset from the UC Irvine Machine Learning Repository demonstrate the accuracies of 76.7% and 78.7% for red and white wines respectively.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.9
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• pp.892-902
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• 2009

An efficient sequential optimization approach for metamodel was presented by Choi et al. This paper describes a new approach of the multilevel optimization method studied in Refs. [2] and [20,21]. The basic idea is concerned with multilevel iterative methods which combine a descent scheme with a hierarchy of auxiliary problems in lower dimensional subspaces. After fitting a metamodel based on an initial space filling design, this model is sequentially refined by the expected improvement criterion. The advantages of the method are that it does not require optimum sensitivities, nonlinear equality constraints are not needed, and the method is relatively easy to understand and use. As a check on effectiveness, the proposed method is applied to an engineering example.

• International Journal of Quality Innovation
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• v.8 no.2
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• pp.115-131
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• 2007

The vehicle development process (VDP) is iterative in nature with numerous interactions and information flows between design groups and between development phases. The VDP has been changed from a sequential-functional development to a concurrent-team based approach. Concurrent execution of design activities may reduce the development lead-time, but it increases the managerial complexity in the VDP. A system dynamics model was developed to understand the transient behavior of parallel, overlap, and sequential processes in the VDP and to determine the optimal level of overlapping considering the development lead-time and total number of reworks. The simulation results showed that different execution processes should be used, depending upon the intensity of reworks.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1520-1526
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• 2014

This paper presents a probabilistic method to evaluate the total transfer capability (TTC) by considering the sequential quadratic programming and the uncertainty of weather conditions. After the initial TTC is calculated by sequential quadratic programming (SQP), the transient stability is checked by time simulation. Also because power systems are exposed to a variety of weather conditions the outage probability is increased due to the weather condition. The probabilistic approach is necessary to evaluate the TTC, and the Monte Carlo Simulation (MCS) is used to accomplish the probabilistic calculation of TTC by considering the various weather conditions.

• Bulletin of the Korean Mathematical Society
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• v.29 no.2
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• pp.301-314
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• 1992

In this paper, motivated by [1] and [7] we give a sequential definition of conditional Wiener integral and then use this definition to evaluate conditional Wiener integral of several functions on C [0, T]. The sequential definition is defined as the limit of a sequence of finite dimensional Lebesgue integrals. Thus the evaluation of conditional Wiener integrals involves no integrals in function space [cf, 5].

• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.20-23
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• 2001

This paper presents a novel design approach composed of two sequential processes for 3D magnetic shielding problems, which results in the global optimum solution in a shorter time. The feature of the proposed approach is the adoption of the specific boundary element with permeability of infinity. Assuming the permeability of infinity enables us to regard the thickness of ferromagnetic shields as infinitesimal, and thus to simplify the investigated model adequately in numerical analysis. This reduces the number of unknown variables and saves us a large amount of CPU-time for grasping the broad characteristics of the model. Some numerical results that demonstrate the validity of the proposed approach are also presented.

• 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.

• 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.

• KIEAE Journal
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• v.9 no.2
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• pp.81-90
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• 2009

Relating to grasping sequential landscape characteristics in consideration of the behavioral characteristics of the subject experiencing visual perception, this study was made on the subject of main walking line section for visitors of three treasures of Buddhist temples. Especially, as a method of obtaining data for grasping sequential visual perception landscape, the researcher employed [momentum sequential viewpoint setup] according to [the interval of pointers arbitrarily] and fisheye-lens-camera photography using the obtained omni-directional angle visual perception information. As a result, in terms of viewpoint selection, factors like approach road form, change in circulation axis, change in the ground surface level, appearance of objects, etc. were verified to make effect, and among these, approach road form and circulation axis change turned out to be the greatest influences. In addition, as a result of reviewing the effectiveness via the subjects, for the sake of qualitative evaluation of landscape components using the VR picture image obtained in the process of acquiring omni-directional angle visual perception information, a positive result over certain values was earned in terms of panoramic vision, scene reproduction, three-dimensional perspective, etc. This convinces us of the possibility to activate the qualitative evaluation of omni-directional angle picture information and the study of landscape through it henceforth.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.9
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• pp.424-433
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• 2001

The cyclic debugging approach often fails for message passing parallel programs because they non-deterministic characteristics due to message race conditions. This paper identifies the MPI events that affect non-deterministic executions, and then converts the concurrent execution to the sequential one that is controlled in order to make it equivalent to a reference execution by keeping their orders of events in two executions identical. This paper also presents an efficient algorithm for the causal distributed breakpoint which is initiated by any sequential breakpoint in one process, and restores each process to the earliest state that reflects all events that happened causally before the sequential breakpoint. So a cyclic debugging approach can be used in debugging MPI parallel programs as like as in debugging sequential programming environments.