• 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.

• Journal for History of Mathematics
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• v.33 no.1
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• pp.1-19
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• 2020

Under 2009 Revised Mathematics Curriculum and 2015 Revised Mathematics Curriculum, mathematics teachers can help students inductively express real life problems related to sequences but have difficulties in dealing with problems asking the general terms of the sequences defined inductively due to 'Guidelines for Teaching and Learning'. Because most of textbooks mainly deal with the simple calculation for the sums of sequences, students tend to follow them rather than developing their inductive and deductive reasoning through finding patterns in the sequences. In this study, we reconstruct 8 problems to find the sums of sequences in MukSaJipSanBup which is known as one of the oldest mathematics book of Chosun Dynasty, using the terminology and symbols of the current curriculum. Such kind of problems can be given in textbooks and used for teaching and learning. Using problems in mathematical books of Chosun Dynasty with suitable modifications for teaching and learning is a good method which not only help students feel the usefulness of mathematics but also learn the cultural value of our traditional mathematics and have the pride for it.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.1
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• pp.61-67
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• 2012

Spreading sequence is used for spreading spectrum in CDMA. For the purpose of minimizing multiple access interference and expanding linear span of the sequences, it is desirable to use such sequences with low correlation and high linear span. To obtain large family size and high linear span, the values of the correlation function of the sequences is more complex. In this paper, we propose the extended Zeng sequences with large family size and high linear span and analyze the correlation of the sequences.

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.727-732
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• 2007

Most of previous work on indexing and searching time series focused on the similarity matching and retrieval of one-attribute time series. However, multimedia databases such as music, video need to handle the similarity search in multi-attribute time series. The limitation of the current similarity models for multi-attribute sequences is that there is no consideration for attributes' sequences. The multi-attribute sequences are composed of several attributes' sequences. Since the users may want to find the similar patterns considering attributes's sequences, it is more appropriate to consider the similarity between two multi-attribute sequences in the viewpoint of attributes' sequences. In this paper, we propose the similarity search method based on attributes's sequences in multi-attribute time series databases. The proposed method can efficiently reduce the search space and guarantees no false dismissals. In addition, we give preliminary experimental results to show the effectiveness of the proposed method.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.29 no.3
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• pp.43-50
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• 1997

The purpose of this study was to decrease pollutants of effluents from KP bleaching. Multistage bleaching that combined with O, Eo, and C/D stage was conducted, after optimum bleaching conditions of O, Eo, and C/D stage were determined. Moreover the properties for bleached pulps and pollution level of bleaching effluent were measured by each standard method. Due to increase on the brightness and delignification of pulps by O, Eo, and C/D stage bleaching. It was possible that bleaching sequences were reduced from five stage to four stage. In addition to, OC/DEoD and OC/DEopD bleaching sequences had higher brightness than CEDED bleaching sequence. The pulps bleached by O and Eo stage had higher in tensile index than conventional bleaching sequences with the similar results in burst and tear index. While each bleaching sequences had little differences, bleaching sequences with Eo stage had higher in physical properties than with Eop stage. The pollution level measured by Chemical Oxygen Demand(COD), Biochemical Oxygen Demand(BOD) and Color of bleaching effluents were much lower in combined bleaching sequences of O, Eo and C/D stage than that of CEDED sequence. Particularly, a pollution index of bleaching effluents generated by C/DEoDEB and OC/DEopDED bleaching sequences were lower than that from the other bleaching 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.

• Journal of the Korean Society for information Management
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• v.19 no.3
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• pp.5-30
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• 2002

In this paper, classification feature is selected with focus of musical content, note sequences pattern, and measures similarity between note sequences followed by constructing clusters by similar note sequences, which is easier for users to search by showing the similar note sequences with the search result in the CBMR system. Experimental document was $\ulcorner$A Dictionary of Musical Themes$\lrcorner$, the index of theme bar focused on classical music and obtained kern-type file. Humdrum Toolkit version 1.0 was used as note sequences treat tool. The hierarchical clustering method is by stages focused on four-type similarity matrices by whether the note sequences segmentation or not and where the starting point is. For the measurement of the result, WACS standard is used in the case of being manual classification and in the case of the note sequences starling from any point in the note sequences, there is used common feature pattern distribution in the cluster obtained from the clustering result. According to the result, clustering with segmented feature unconnected with the starting point Is higher with distinct difference compared with clustering with non-segmented feature.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.14 no.4
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• pp.11-23
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• 2011

Mountain streams, which are major components of an entire river network, play an important role as the source of water, sediment, coarse and fine organic matter, and nutrients for lowland rivers. Therefore, dynamics and downstream linkages of each compartment of the mountain stream can be essential for watershed management in catchment scale. The dynamics and downstream linkages are understood as a development of step-pool sequences along a river course. Recently, stream restoration after flooding event often employ the development of step-pool sequences in the world. In this paper, we 1) examined the geomorphic characteristics and the role of step-pool sequences in steep mountain streams by reviewing the results of past studies, and 2) introduced the case studies of stream restoration using step-pool sequences, and finally 3) addressed design methods considering geometry and stability of artificial step-pool sequences for stream restoration. Step-pool sequences play an important role not only as roughness with energy dissipation but also as heterogeneity of stream feature for aquatic habitat. Step-pool sequences, even if they are constructed artificially along a stream, may be effective for small stream restoration considering eco-friendly torrent controls. So far the artificial step-pool sequences were employed for mountainous streams, but those would be applied to urban stream.

• Journal of Educational Research in Mathematics
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• v.12 no.4
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• pp.557-579
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• 2002

The purpose of this study is to find out the problems which are caused when the limit concept of sequences is learned through an intuitive definition and to suggest a way of solving those problems. Students in Korea study the limit concept of sequences through an intuitive definition. They fail to apply the intuitive definition properly to the problems and they are apt to have misconception even though the Intuitive definition is applied properly. To solve these problems, this study examined the develop- mental process of the limit concept of sequences from the Intuitive definition to the formal definition, and looked into the way of students' internalization of the process through a field study. In this study, the levels of the limit concept of sequences possessed by the students at ZPD are as follows; level 0 : Students understand the limit concept of sequences through the intuitive definition. level 1 : Students understand the limit concept of sequences as 'The difference between $\alpha$$_{n}$ and $\alpha$ approaches 0' rather than 'The sequence approaches $\alpha$ infinitely.' level 2 : Students understand the limit concept of sequences through the formal definition. The levels of students' limit concept development were analysed by those criteria. Almost of the students who studied the limit concept of sequences through the intuitive defition stayed at level 0, whereas almost of the students who studied through the formal definition stayed at level 1. Through the study, I found that it was difficult for the students to develop the higher level of understanding for themselves but the teachers and peers could help the students to progress to the higher level. Students' learning ability was one of major factors that make the students progress to the higher level of understanding as the concept was developed hierarchically from Level 0 to Level 2. If you want to see your students get to the higher level of understanding in the limit concept, you need to facilitate them to fully develop understanding in lower levels through enough experiences so that they can be promoted to the highest level.

• Investigative Magnetic Resonance Imaging
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• v.22 no.4
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• pp.229-239
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• 2018

Purpose: To assess the diagnostic performance in detecting rotator cuff tears at 3T of non-arthrographic shoulder magnetic resonance imaging (MRI) using 3D isotropic turbo spin-echo (TSE-SPACE) sequence as compared with 2D sequences. Materials and Methods: Seventy-four patients who were arthroscopically confirmed to have underwent non-arthrographic shoulder MRI with 2D sequences and TSE-SPACE were included. Three independent readers retrospectively scored supraspinatus and infraspinatus tendon (SST-IST) and subscapularis tendon (SCT) tears on 2D sequences and TSE-SPACE. Results: The mean sensitivity, specificity, and accuracy of the three readers were 95%, 100%, and 95% on TSE-SPACE and 99%, 93%, and 98% on 2D sequences for detecting SST-IST tears, respectively, whereas those were 87%, 49%, and 68% on TSESPACE and 88%, 66%, and 77% on 2D sequences for detecting SCT tears, respectively. There was no statistical difference between the two sequences, except for in the specificity of one reader for detecting SCT tears. The mean AUCs of the three readers on TSE-SPACE and 2D sequences were 0.96 and 0.98 for detecting SST-IST tears, respectively, which were not significantly different, while those were 0.71 and 0.82 for detecting SCT tears, respectively, which were significantly different (P < 0.05). Conclusion: TSE-SPACE may have accuracy and reliability comparable to conventional 2D sequences for SST-IST tears at non-arthrographic 3T shoulder MRI, whereas TSE-SPACE was less reliable than conventional 2D sequences for detecting SCT tears.