• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.1-12
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• 2003

Recently, there have been many indexing schemes for multimedia data such as image, video data. But recent database applications, for example data mining and multimedia database, are required to support multi-user environment. In order for indexing schemes to be useful in multi-user environment, a concurrency control algorithm is required to handle it. So we propose a concurrency control algorithm that can be applied to CBF (cell-based filtering method), which uses the signature of the cell for alleviating the dimensional curse problem. In addition, we extend the SHORE storage system of Wisconsin university in order to handle high-dimensional data. This extended SHORE storage system provides conventional storage manager functions, guarantees the integrity of high-dimensional data and is flexible to the large scale of feature vectors for preventing the usage of large main memory. Finally, we implement the web-based image retrieval system by using the extended SHORE storage system. The key feature of this system is platform-independent access to the high-dimensional data as well as functionality of efficient content-based queries. Lastly. We evaluate an average response time of point query, range query and k-nearest query in terms of the number of threads.

• Journal of Korea Water Resources Association
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• v.34 no.4
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• pp.303-316
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• 2001

In this study, Radial Basis Function(RBF) Neural Networks Model, a kind of Hybrid Neural Networks was applied to hydrological forecasting in a small watershed. RBF Neural Networks Model has four kinds of parameters in it and consists of unsupervised and supervised training patterns. And Gaussian Kernel Function(GKF) was used among many kinds of Radial Basis Functions(RBFs). K-Means clustering algorithm was applied to optimize centers and widths which ate the parameters of GKF. The parameters of RBF Neural Networks Model such as centers, widths weights and biases were determined by the training procedures of RBF Neural Networks Model. And, with these parameters the validation procedures of RBF Neural Networks Model were carried out. RBF Neural Networks Model was applied to Wi-Stream basin which is one of the IHP Representative basins in South Korea. 10 rainfall events were selected for training and validation of RBF Neural Networks Model. The results of RBF Neural Networks Model were compared with those of Elman Neural Networks(ENN) Model. ENN Model is composed of One Step Secant BackPropagation(OSSBP) and Resilient BackPropagation(RBP) algorithms. RBF Neural Networks shows better results than ENN Model. RBF Neural Networks Model spent less time for the training of model and can be easily used by the hydrologists with little background knowledge of RBF Neural Networks Model.

• Journal of Life Science
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• v.20 no.6
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• pp.934-939
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• 2010

We have previously reported recombinant productions of bacteriocins using yeast expression plasmid pAUR123, which contains the alcohol dehydrogenase (ADH) promoter, in Saccharomyces cerevisiae cells and their antibacterial activities. In order to improve the antibacterial activities of bacteriocidal yeast cells, a strong glyceraldehyde phosphate dehydrogenase (GPD) promoter gene of S. cerevisiae was amplified and inserted upstream into bacteriocin genes such as the OR-7, Subpeptin JM4-A or JM4-B gene in the corresponding recombinant yeast plasmid. Yeast cells transformed by the recombinant plasmid containing the GPD promoter represented higher antibacterial activities against both Gram positive B. subtilis and Gram negative E. coli cells compared to those transformed by the corresponding recombinant plasmid containing the ADH promoter. Thus, yeast cells harboring the recombinant plasmid containing the GPD promoter constructed in this study could be applied in the food preservative or animal feed industries.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.311-316
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• 2016

Network intrusion detection system based on machine learning method such as artificial neural network is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features, which guarantees accuracy and efficienty, from generally used many features to detect network intrusion requires extensive computing resources. In this paper, we deal with a optimal feature selection problem to determine 6 denial service attacks and normal usage provided by NSL-KDD data. We propose a optimal feature selection algorithm. Proposed algorithm is based on the multi-start local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In order to evaluate the performance of our proposed algorithm, comparison with a case of all 41 features used against NSL-KDD data is conducted. In addtion, comparisons between 3 well-known machine learning methods (multi-layer perceptron., Bayes classifier, and Support vector machine) are performed to find a machine learning method which shows the best performance combined with the proposed feature selection method.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.637-644
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• 2011

Being digitalized, the music can be easily purchased and delivered to the users. However, there is still some difficulty to find the music which fits to someone's taste using traditional music information search based on musician, genre, tittle, album title and so on. In order to reduce the difficulty, the contents-based or the emotion-based music retrieval has been proposed and developed. In this paper, we propose new method to determine the importance of MPEG-7 low-level audio descriptors which are multi-dimensional vectors for the emotion-based music retrieval. We measured the mutual similarities of musics which represent a pair of emotions expressed by opposite meaning in terms of each multi-dimensional descriptor. Then rough approximation, and inter- and intra similarity ratio from the similarity relation are used for determining the importance of a descriptor, respectively. The set of weights based on the importance decides the aggregated similarity measure, by which emotion-based music retrieval can be achieved. The proposed method shows better result than previous method in terms of the average number of satisfactory musics in the experiment emotion-based retrieval based on content-based search.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.187-193
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• 2015

The ICT healing platform sets a couple of goals including preventing chronic diseases and sending out early disease warnings based on personal information such as bio-signals and life habits. The 2-step open system(TOS) had a relay designed between the healing platform and the storage of personal health data. It also took into account a publish/subscribe(pub/sub) service based on large-scale connections to transmit(monitor) the data processing process in real time. In the early design of TOS pub/sub, however, the same buffers were allocated regardless of connection idling and type of message in order to encode connection messages into a deflate algorithm. Proposed in this study, the dynamic buffer allocation was performed as follows: the message transmission type of each connection was first put to queuing; each queue was extracted for its feature, computed, and converted into vector through tf-idf, then being entered into a k-means cluster and forming a cluster; connections categorized under a certain cluster would re-allocate the resources according to the resource table of the cluster; the centroid of each cluster would select a queuing pattern to represent the cluster in advance and present it as a resource reference table(encoding efficiency by the buffer sizes); and the proposed design would perform trade-off between the calculation resources and the network bandwidth for cluster and feature calculations to efficiently allocate the encoding buffer resources of TOS to the network connections, thus contributing to the increased tps(number of real-time data processing and monitoring connections per unit hour) of TOS.

• KSBB Journal
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• v.24 no.2
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• pp.182-188
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• 2009

Lactobacillus spp., primary members of probiotics, have significant benefits for health and well-being of human. In this study Lactobacillus strains representing six species (L. paracasei KLB58, L. fermentum MS79 and KLB282, L. plantarum KLB213, L. gasseri KLB238, and L. reuteri KLB270) isolated from Korean adults were electrotransformed with plasmid pNCKH104. To determine optimal electrotransformation conditions, various conditions including cell wall weakening agent, electroporation buffer, electric field strength and time constant were tested for each strain. Overall, high transformation efficiency of approximately 2.5 ${\times}$ $10^3$ ${\sim}$ 5.5 ${\times}$ $10^4$ CFU/${\mu}g$ DNA was obtained where conditions of 0.5 M sucrose electroporation buffer, 1.8 kV pulse voltage and 5 ms time constant were applied. The common conditions developed in this study will make transformation of various Lactobacillus spp. easier than previous procedures.

• Journal of Radiation Protection and Research
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• v.16 no.2
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• pp.41-54
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• 1991

This paper aimed to analyse dose sensitivity to the controllable parameters of indoor radon $(^{222}Rn)$ and its decay products (Rn-D) by applying the input~output linear system theory. Physical behaviors of $^{222}Rn\;&\;Rn-D$ were analyzed in terms of $(^{222}Rn)$ gas -generation, -migation and -infiltration to indoor environments, and the performance output-function, i. e. mean dose equivalent to Tracho-Bronchial (TB) lung region, was assessed to the following extented ranges of the controllable paramenters; a) the ventilation rate $constant({\lambda}_v)\;:\;0{\sim}50[h^{-l}].\;b)$ the attachment rate $constant({\lambda}_a)\;:\;0{\sim}500[h^{-l}].\;c)$ the unattached-deposition rate constant (${\lambda}^u_d)\;:\;0-50[h-l]$. A linear input-output model was reconstructed from the original models in literatures, as follows, which was modified into the matrices consisting of 111 nodal equations; a) indoor $^{222}Rn\;&\;Rn-D$ Behaviour; Jacobi-Porstendoerfer-Bruno model.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.819-829
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• 2019

In the disaster of collapse, an immediate response is needed to prevent the damage from worsening, and damage area calculation, response and recovery plan should be established. This requires accurate detection of the damage affected area. This study performed the detection of the damaged area by using UAV which can respond quickly and in real-time to detect the collapse accident. The study area was selected as B-05 housing redevelopment area in Jung-gu, Ulsan, where the demolition of houses and apartments in progress as the redevelopment project began. This area resembles a collapsed state of the building, which clear changes before and after the demolition. UAV images were acquired on May 17 and July 9, 2019, respectively. The changing area was considered as the damaged area before and after the collapse of the building, and the changing area was detected using CVA (Change Vector Analysis) the Representative Change Detection Technique, and SLIC (Simple Linear Iterative Clustering) based superpixel algorithm. In order to accurately perform the detection of the damaged area, the uninterested area (vegetation) was firstly removed using ExG (Excess Green), Among the objects that were detected by change, objects that had been falsely detected by area were finally removed by calculating the minimum area. As a result, the accuracy of the detection of damaged areas was 95.39%. In the future, it is expected to be used for various data such as response and recovery measures for collapse accidents and damage calculation.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.27-35
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• 2021

With the development of the Internet, various IT technologies such as IoT, Cloud, etc. have been developed, and various systems have been built in countries and companies. Because these systems generate and share vast amounts of data, they needed a variety of systems that could detect threats to protect the critical data contained in the system, which has been actively studied to date. Typical techniques include anomaly detection and misuse detection, and these techniques detect threats that are known or exhibit behavior different from normal. However, as IT technology advances, so do technologies that threaten systems, and these methods of detection. Advanced Persistent Threat (APT) attacks national or companies systems to steal important information and perform attacks such as system down. These threats apply previously unknown malware and attack technologies. Therefore, in this paper, we propose a hybrid intrusion detection system that combines anomaly detection and misuse detection to detect unknown threats. Two detection techniques have been applied to enable the detection of known and unknown threats, and by applying machine learning, more accurate threat detection is possible. In misuse detection, we applied Classification based on Association Rule(CBA) to generate rules for known threats, and in anomaly detection, we used One-Class SVM(OCSVM) to detect unknown threats. Experiments show that unknown threat detection accuracy is about 94%, and we confirm that unknown threats can be detected.