• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1554-1574
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• 2017

This paper introduces a new type of determining factor for Pseudo Random Strings (PRS). This classification depends upon a mathematical property called Finite Induction (FI). FI is similar to a Markov Model in that it presents a model of the sequence under consideration and determines the generating rules for this sequence. If these rules obey certain criteria, then we call the sequence generating these rules FI a PRS. We also consider the relationship of these kinds of PRS's to Good/deBruijn graphs and Linear Feedback Shift Registers (LFSR). We show that binary sequences from these special graphs have the FI property. We also show how such FI PRS's can be generated without consideration of the Hamiltonian cycles of the Good/deBruijn graphs. The FI PRS's also have maximum Shannon entropy, while sequences from LFSR's do not, nor are such sequences FI random.

• Journal of KIISE:Databases
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• v.33 no.4
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• pp.423-436
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• 2006

In recent years, publish-subscribe (pub-sub) systems based on XML document filtering have received much attention. In a typical pub-sub system, subscribing users specify their interest in profiles expressed in the XPath language, and each new content is matched against the user profiles so that the content is delivered only to the interested subscribers. As the number of subscribed users and their profiles can grow very large, the scalability of the system is critical to the success of pub-sub services. In this paper, we propose a novel scalable filtering system called FiST(Filtering by Sequencing Twigs) that transforms twig patterns expressed in XPath and XML documents into sequences using Prufer's method. As a consequence, instead of matching linear paths of twig patterns individually and merging the matches during post-processing, FiST performs holistic matching of twig patterns with incoming documents. FiST organizes the sequences into a dynamic hash based index for efficient filtering. We demonstrate that our holistic matching approach yields lower filtering cost and good scalability under various situations.

• Korean Journal of Mathematics
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• v.22 no.3
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• pp.443-454
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• 2014

In this paper, we give linearization of generalized Fi-bonacci sequences {$g_n$} and {$q_n$}, respectively, defined by Eq.(5) and Eq.(6) below and use this result to give the matrix form of the nth power of a companion matrix of {$g_n$} and {$q_n$}, respectively. Then we re-prove the Cassini's identity for {$g_n$} and {$q_n$}, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.1
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• pp.5-21
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• 2008

In order to securely transmit multicast data packets in a mobile environment where frequent join/leave events are a characteristic of the environment, there is a need for a new secure and efficient group key management solution. We propose a secure group key generation/distribution solution providing scalability and reliability. Using this solution, when a mobile node, which is in a multicast session, enters a new domain, the agent of the domain joins the multicast session and coordinates its data packets with the mobile node. The agent encrypts and transmits subsequent data packets to the mobile node, using a local one-time pad key. This key is generated with FI sequences, enabling the mobile node to regenerate the same data packet, based on the information sent by the agent. Our performance analysis demonstrates that the proposed solution can significantly reduce the number of key generations and distributions, when it is applied to the hierarchical mobile IPv6 network.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.368-378
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• 2008

In recent years, publish-subscribe (pub-sub) systems based on XML document filtering have received much attention. In a typical pub-sub system, subscribed users specify their interest in profiles expressed in the XPath language, and each new content is matched against the user profiles so that the content is delivered to only the interested subscribers. As the number of subscribed users and their profiles can grow very large, the scalability of the system is critical to the success of pub-sub services. In this paper, we propose a fast and scalable XML filtering system called SFiST which is an extension of the FiST system. Sharable segments are extracted from twig patterns and stored into the hash-based Segment Table in SFiST system. Segments are used to represent user profiles as Terse Sequences and stored in the Compact Segment Index during filtering. Our experimental study shows that SFiST system has better performance than FiST system in terms of filtering time and memory usage.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.7
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• pp.1126-1132
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• 2002

In this paper, we propose a new scheme of motion tracking based on fuzzy inference (Fl) and wavelet transform (WT) from image sequences. First, we present a WT to segment a feature extraction of dynamic image . The coefficient matrix for 2-level DWT tent to be clustered around the location of Important features in the images, such as edge discontinuities, peaks, and corners. But these features are time varying owing to the environment conditions. Second, to reduce the spatio-temperal error, We develop a fuzzy inference algorithm. Some experiments are performed 0 testify the validity and applicability of the proposed system As a result, proposed method is relatively simple compared with the traditional space domain method. It is also well suited for motion tracking under the conditions of variation of illumination.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.217-221
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• 2002

In this paper, we propose a new scheme of motion tracking based on fuzzy inference (FI)and wavelet transform (WT) from image sequences. First, we present a WT to segment a feature extraction of dynamic image . The coefficient matrix for 2-level DWT tent to be clustered around the location of important features in the images, such as edge discontinuities, peaks, and corners. But these features are time varying owing to the environment conditions. Second, to reduce the spatio-temporal error, We develop a fuzzy inference algorithm. Some experiments are peformed to testify the validity and applicability of the proposed system. As a result, proposed method is relatively simple compared with the traditional space domain method. It is also well suited for motion tracking under the conditions of variation of illumination.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.57-65
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• 2018

Conventional RF signal-based indoor localization techniques such as BLE or Wi-Fi based fingerprinting method show considerable localization errors even in small-scale indoor environments due to unstable received signal strength(RSS) of RF signals. Therefore, it is difficult to apply the existing RF-based fingerprinting techniques to large-scale indoor environments such as airports and department stores. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We use a deep neural network model called the recurrent neural network (RNN), which is effective in recognizing time-varying sequences of sensor data, to track the user's location and movement path. To evaluate the performance of the proposed geomagnetic field based indoor positioning system (IPS), we constructed a magnetic field map for a campus testbed of about $94m{\times}26$ dimension and trained RNN using various potential movement paths and their location data extracted from the magnetic field map. By adjusting various hyperparameters, we could achieve an average localization error of 1.20 meters in the testbed.

• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 2000.11a
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• pp.59-75
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• 2000

This study was carried out to develop a DNA marker for identifying between Korean cattle (Hanwoo) and other breeds. First experiment was performed to isolate Hanwoo specific DNA marker at sequence characterized amplified regions (SCARs). Five breeds of cattle including Hanwoo, Holstein, Hereford, Angus and Charolais were represented with the from 8 to 20 individuals. Fourteen primers of 300 arbitrary primers of 10 nucleotides showed reproducible polymorphism across the breeds. An amplified band of 0.9 kb in the primer MG-3 showed the specificity to Holstein breed. And MG-6 and MG-12 detected the Hereford and Hanwoo specific markers at the size of 2.0 kb and 1.0 kb, respectively. A 1.0 kb band of MG-12 was cloned and sequenced. A SCAR primer was designed based on the obtained sequences. It was possible to identify the Hanwoo from Holstein breed. Second experiment was carried out to observe the genotype frequencies of MC1R in 1,044 samples of imported beef and eight different cattle breeds including Hanwoo, Holstein, Angus, Brown-Swiss, Charolais, Limousin, Simmental and Hereford. The primers for the amplification of bovine MC1R gene were designed based on a bovine MC1R gene sequence (GenBank accession no.Y19103). A size of 350 bp was amplified by polymerase chain reaction(PCR), digested with two different restriction enzyme, BsrFI and MspA II, and electrophoresed in 2.5% Metaphore agarose gel for determination of genotypes. Genotype frequencies of Hanwoo were 0.10 in E+e and 0.90 in ee. Allele ED was shown in all of Holstein and Angus breeds tested which have black coat color phenotypes. We suggested that SCAR marker and the bovine MC1R gene could be used as a DNA marker for distinguishing beef between Hanwoo and Holstein.