• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.4
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• pp.296-311
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• 2021

Topological Data Analysis (TDA), a relatively new field of data analysis, has proved very useful in a variety of applications. The main persistence tool from TDA is persistent homology in which data structure is examined at many scales. Representations of persistent homology include persistence barcodes and persistence diagrams, both of which are not straightforward to reconcile with traditional machine learning algorithms as they are sets of intervals or multisets. The problem of faithfully representing barcodes and persistent diagrams has been pursued along two main avenues: kernel methods and vectorizations. One vectorization is the Betti sequence, or Betti curve, derived from the persistence barcode. While the Betti sequence has been used in classification problems in various applications, to our knowledge, the stability of the sequence has never before been discussed. In this paper we show that the Betti sequence is unstable under the 1-Wasserstein metric with regards to small perturbations in the barcode from which it is calculated. In addition, we propose a novel stabilized version of the Betti sequence based on the Gaussian smoothing seen in the Stable Persistence Bag of Words for persistent homology. We then introduce the normalized cumulative Betti sequence and provide numerical examples that support the main statement of the paper.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.413-424
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• 2011

A variety of different metrics has been introduced to measure the similarity of two given sequences. These widely used metrics are ranging from spell correctors and categorizers to new sequence mining applications. Different metrics consider different aspects of sequences, but the essence of any sequence is extracted from the ordering of its elements. In this paper, we propose a novel sequence similarity measure that is based on all ordered pairs of one sequence and where a Hasse diagram is built in the other sequence. In contrast with existing approaches, the idea behind the proposed sequence similarity metric is to extract all ordering features to capture sequence properties. We designed a clustering problem to evaluate our sequence similarity metric. Experimental results showed the superiority of our proposed sequence similarity metric in maximizing the purity of clustering compared to metrics such as d2, Smith-Waterman, Levenshtein, and Needleman-Wunsch. The limitation of those methods originates from some neglected sequence features, which are considered in our proposed sequence similarity metric.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.181-186
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• 2012

Recently there has been many active researches on searching similar sequences from data generated with the passage of time. Those data are classified as time series data or sequence data and have different semantics from scalar data of traditional databases. In this paper similar sequence search retrieves sequences that have a similar trend of value changes. At first we have transformed the original sequences by applying DFT. The converted data are more suitable for trend analysis and they require less number of attributes for sequence comparisons. In addition we have developed a region-based query and we applied bitmap indexes which could show better performance in data warehouse. We have built bitmap indexes with varying number of attributes and we have found the least cost query plans for efficient similar sequence searches.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.2-4
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• 2023

Sequence data plays an important role in the field of intelligence, especially for industrial control, traffic control and other aspects. Finding abnormal parts in sequence data has long been an application field of AI technology. In this paper, we propose an anomaly detection method for sequence data using a diffusion model. The diffusion model has two major advantages: interpretability derived from rigorous mathematical derivation and unrestricted selection of backbone models. This method uses the diffusion model to predict and reconstruct the sequence data, and then detects the abnormal part by comparing with the real data. This paper successfully verifies the feasibility of the diffusion model in the field of anomaly detection. We use the combination of MLP and diffusion model to generate data and compare the generated data with real data to detect anomalous points.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.569-579
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• 2006

Recently, the amount of genome sequence is increasing rapidly due to advanced computational techniques and experimental tools in the biological area. Sequence comparisons are very useful operations to predict the functions of the genes or proteins. However, it takes too much time to compare long sequence data and there are many research results for fast sequence comparisons. In this paper, we propose a hybrid grid system to improve the performance of the sequence comparisons based on the LanLinux system. Compared with conventional approaches, hybrid grid is easy to construct, maintain, and manage because there is no need to install SWs for every node. As a real experiment, we constructed an orthologous database for 89 prokaryotes just in a week under hybrid grid; note that it requires 33 weeks on a single computer.

• Genomics & Informatics
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• v.6 no.3
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• pp.157-159
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• 2008

Multiple sequence alignment (MSA) is the most important step for many of biological sequence analyses, homology search, and protein structural assignments. However, large amount of data make biologists difficult to perform MSA analyses and it requires much computational time to align many sequences. Here, we have developed a simple and fast web alignment tool for aligning, editing, and visualizing large amount of sequence data. We used a cluster server installed ClustalW-MPI using web services and message passing interface (MPI). It also enables users to edit multiple sequence alignments for manual editing and to download the input data and results such as alignments and phylogenetic tree.

• Journal of the Korean Institute of Telematics and Electronics
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• v.12 no.2
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• pp.1-10
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• 1975

This paper presents an adaptive data compression algorithm for video data. The coling complexity due to the high correlation in the given data sequence is alleviated by coding the difference data, sequence rather than the data sequence itself. The adaptation to the nonstationary statistics of the data is confined within a code set, which consists of two constant length cades and six modified Shannon.Fano codes. lt is assumed that the probability distributions of tile difference data sequence and of the data entropy are Laplacian and Gaussion, respectively. The adaptive coding performance is compared for two code selection criteria: entropy and $P_r$[difference value=0]=$P_0$. It is shown that data compression ratio 2 : 1 is achievable with the adaptive coding. The gain by the adaptive coding over the fixed coding is shown to be about 10% in compression ratio and 15% in code efficiency. In addition, $P_0$ is found to he not only a convenient criterion for code selection, but also such efficient a parameter as to perform almost like entropy.

• ALGAE
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• v.26 no.3
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• pp.229-235
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• 2011

Spliced leader (SL) trans-splicing is a mRNA processing mechanism in dinoflagellate nuclear genes. Although studies have identified a short, conserved dinoflagellate SL (dinoSL) sequence (22-nt) in their nuclear-encoded transcripts, whether the majority of nuclear-coded transcripts in dinoflagellates have the dinoSL sequence remains doubtful. In this study, we investigated dinoSL-containing gene transcripts using 454 pyrosequencing data (Cochlodinium polykrikoides, 93 K sequence reads, 31 Mb; Prorocentrum minimum, 773 K sequence reads, 291 Mb). After making comparisons and performing local BLAST searches, we identified dinoSL for one C. polykrikoides gene transcript and eight P. minimum gene transcripts. This showed transcripts containing the dinoSL sequence were markedly fewer in number than the total expressed sequence tag (EST) transcripts. In addition, we found no direct evidence to prove that most dinoflagellate nuclear-coded transcripts have this dinoSL sequence.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1554-1563
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• 1999

This paper presents the design of high speed processor for a sequence logic control using field programmable gate array(FPGA). The sequence logic controller is widely used for automating a variety of industrial plants. The FPGA designed by VHDL consists of program and data memory interface block, input and output block, instruction fetch and decoder block, register and ALU block, program counter block, debug control block respectively. Dedicated clock inputs in the FPGA were used for high speed execution, and also the program memory was separated from the data memory for high speed execution of the sequence instructions at 40 MHz clock. Therefore it was possible that sequence instructions could be operated at the same time during the instruction fetch cycle. In order to reduce the instruction decoding time and the interface time of the data memory interface, an instruction code size was implemented by 16 bits or 32 bits respectively. And the real time debug operation was implemented for easy debugging the designed processor. This FPGA was synthesized by pASIC 2 SpDE and Synplify-Lite synthesis tool of Quick Logic company. The final simulation for worst cases was successfully performed under a Verilog HDL simulation environment. And the FPGA programmed for an 84 pin PLCC package was applied to sequence control system with inputs and outputs of 256 points. The designed processor for the sequence logic was compared with the control system using the DSP(TM320C32-40MHz) and conventional PLC system. The designed processor for the sequence logic showed good performance.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.221-229
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• 2004

Recently, there has been enormous growth in the amount of commercial and scientific data, such as protein sequences, retail transactions, and web-logs. Such datasets consist of sequence data that have an inherent sequential nature. However, only a few of the existing clustering algorithms consider sequentiality. This study presents a method for clustering such sequence datasets. The similarity between sequences must be decided before clustering the sequences. This study proposes a new similarity measure to compute the similarity between two sequences using a sequence element. Two clustering algorithms using the proposed similarity measure are proposed: a hierarchical clustering algorithm and a scalable clustering algorithm that uses sampling and a k-nearest neighbor method. Using a splice dataset and synthetic datasets, we show that the quality of clusters generated by our proposed clustering algorithms is better than that of clusters produced by traditional clustering algorithms.