• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.28 no.2
• /
• pp.437-447
• /
• 2018

Virtual Reality (VR) is becoming a new standard in the game industry. PokeMon GO is a representative example of VR technology. The day after the launch of PokeMon Go in the U.S, It has achieved the highest number of iOS App Store downloads. This is an example of the power of VR. VR comprises gyroscopes, acceleration, tactile sensors, and so on. This allow users could be immersed in the game. As new technologies emerge, new and different threats are created. So we need to research the security of VR technology and game system. In this paper, we conduct a threat analysis for information assurance of VR device (Oculus Rift) and game system (Quake). We systematically analyze the threats (STRIDE, attack library, and attack tree). We propose security measures through DREAD. In addition, we use Visual Code Grepper (VCG) tool to find out logic errors and vulnerable functions in source code, and propose a method to solve them.

• Journal of KIISE:Databases
• /
• v.29 no.1
• /
• pp.79-88
• /
• 2002

Data on the Internet are usually represented and transfered as XML. the XML data is represented as a tree and therefore, object repositories are well-suited to store and query them due to their modeling power. XML queries are represented as regular path expressions and evaluated by traversing each object of the tree in object repositories. Several indexes are proposed to fast evaluate regular path expressions. However, in some cases they may not cover all possible paths because they require a great amount of disk space. In order to efficiently evaluate the queries in such cases, we propose an optimized traversing which combines the signature method and block traversing. The signature approach shrink the search space by using the signature information attached to each object, which hints the existence of a certain label in the sub-tree. The block traversing reduces disk I/O by early evaluating the reachable objects in a page. We conducted diverse experiments to show that the hybrid approach achieves a better performance than the other naive ones.

• Journal of Digital Convergence
• /
• v.15 no.6
• /
• pp.229-236
• /
• 2017

An efficient frequent pattern algorithm is essential for mining association rules as well as many other mining tasks for convergence with its application spread over a very broad spectrum. Models for mining pattern have been proposed using a FP-tree for storing compressed information about frequent patterns. In this paper, we propose a centroid frequent pattern growth algorithm which we called "CAWFP-Growth" that enhances he FP-Growth algorithm by making the center of weights and frequencies for the itemsets. Because the conventional constraint of maximum weighted support is not necessary to maintain the downward closure property, it is more likely to reduce the search time and the information loss of the frequent patterns. The experimental results show that the proposed algorithm achieves better performance than other algorithms without scarifying the accuracy and increasing the processing time via the centroid of the items. The MapReduce framework model is provided to handle large amounts of data via a pseudo-distributed computing environment. In addition, the modeling of the proposed algorithm is required in the fully distributed mode.

• Journal of the Korea Concrete Institute
• /
• v.14 no.6
• /
• pp.1022-1031
• /
• 2002

This paper presents efficient models for bridge structures using CART-ANFIS (classification and regression tree-adaptive neuro fuzzy inference system). A fuzzy decision tree partitions the input space of a data set into mutually exclusive regions, each region is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it continuous and smooth everywhere. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.40 no.4
• /
• pp.221-226
• /
• 2016

Since arterial disease in the upper extremity is less common than that in the lower extremity, experimental and numerical investigations related to upper extremity have been rarely performed. We created a three-dimensional model of the arteries, larger than approximately 1 mm, in a Korean adult's left hand (from brachial to digital arteries), from 3T magnetic resonance imaging (MRI) data. For the first time, a three-dimensional computational fluid dynamic method was employed to investigate blood flow velocity, blood pressure variation, and wall shear stress (WSS) on this complicated artery system. Investigations were done on physiological blood flows near the branches of radial and deep palmar arch arteries, and ulnar and superficial palmar arch arteries. The flow is assumed to be laminar and the fluid is assumed to be Newtonian, with density and viscosity properties of plasma.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.3
• /
• pp.259-268
• /
• 2012

This paper aims to estimate the carbon dioxide stocks in forests using airborne LiDAR data with a density of approximate 4.4 points per meter square. To achieve this goal, a processing chain consisting of bare earth Digital Terrain Model(DTM) extraction and individual tree top detection has been developed. As results of this experiment, the reliable DTM with type-II errors of 3.32% and tree positions with overall accuracy of 66.26% were extracted in the study area. The total estimated carbon dioxide stocks in the study area using extracted 3-D forests structures well suited with the traditional method by field measurements upto 7.2% error level. This results showed that LiDAR technology is highly valuable for replacing the existing forest resources inventory.

• STI Policy Review
• /
• v.8 no.2
• /
• pp.75-93
• /
• 2017

Recently, global competition in the manufacturing sector is driving firms in the manufacturing sector to conduct product innovation projects to maintain their competitive edge. The key points of product innovation projects are 1) what the purpose of the project is and 2) what expected results in the target market can be achieved by implementing the innovation. Therefore, this study focuses on the performance of innovation projects with a business viewpoint. In this respect, this study proposes the "achievement rate" of product innovation projects as a measurement of project performance. Then, this study finds the best strategies from various innovation activities to optimize the achievement rate of product innovation projects. There are three major innovation activities for the projects, including three types of R&D activities: Internal, joint and external R&D, and five types of non-R&D activities - acquisition of machines, equipment and software, purchasing external knowledge, job education and training, market research and design. This study applies decision tree modeling, a kind of data-mining methodology, to explore effective innovation activities. This study employs the data from the 'Korean Innovation Survey (KIS) 2014: Manufacturing Sector.' The KIS 2014 gathered information about innovation activities in the manufacturing sector over three years (2011-2013). This study gives some practical implication for managing the activities. First, innovation activities that increased the achievement rate of product diversification projects included a combination of market research, new product design, and job training. Second, our results show that a combination of internal R&D, job training and training, and market research increases the project achievement most for the replacement of outdated products. Third, new market creation or extension of market share indicates that launching replacement products and continuously upgrading products are most important.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.26 no.3
• /
• pp.29-42
• /
• 2023

As the destruction of habitats due to recent development continues, there is also increasing interest in endangered species. Mankyua chejuense is a vulnerable species that is sensitive to changes in population and habitat, and it has recently been upgraded from Endangered Species II to Endangered Species I, requiring significant management efforts. So in this study, we analyzed the potential habitats of Mankyua chejuense using MaxEnt(Maximum Entropy) modeling. We developed three models: one that considered only environmental characteristics, one that considered artificial factors, and one that reflected the habitat of dominant tree species in the overstory. Based on previous studies, we incorporated environmental and human influence factors for the habitats of Mankyua chejuense into spatial information, and we also used the habitat distribution models of dominant tree species, including Ulmus parvifolia, Maclura tricuspidata, and Ligustrum obtusifolium, that have been previously identified as major overstory species of Mankyua chejuense. Our analysis revealed that rock exposure, elevation, slope, forest type, building density, and soil type were the main factors determining the potential habitat of Mankyua chejuense. Differences among the three models were observed in the edges of the habitats due to human influence factors, and results varied depending on the similarity of the habitats of Mankyua chejuense and the dominant tree species in the overstory. The potential habitats of Mankyua chejuense presented in this study include areas where the species could potentially inhabit in addition to existing habitats. Therefore, these results can be used for the conservation and management planning of Mankyua chejuense.

• Journal of the Institute of Convergence Signal Processing
• /
• v.4 no.4
• /
• pp.30-39
• /
• 2003

In this paper, we carried out the performance evaluation of HM-Net(Hidden Markov Network) speech recognition system for Korean speech databases. We adopted to construct acoustic models using the HM-Nets modified by HMMs(Hidden Markov Models), which are widely used as the statistical modeling methods. HM-Nets are carried out the state splitting for contextual and temporal domain by PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) algorithm, which is modified the original SSS algorithm. Especially it adopted the phonetic decision tree to effectively express the context information not appear in training speech data on contextual domain state splitting. In case of temporal domain state splitting, to effectively represent information of each phoneme maintenance in the state splitting is carried out, and then the optimal model network of triphone types are constructed by in the parameter. Speech recognition was performed using the one-pass Viterbi beam search algorithm with phone-pair/word-pair grammar for phoneme/word recognition, respectively and using the multi-pass search algorithm with n-gram language models for sentence recognition. The tree-structured lexicon was used in order to decrease the number of nodes by sharing the same prefixes among words. In this paper, the performance evaluation of HM-Net speech recognition system is carried out for various recognition conditions. Through the experiments, we verified that it has very superior recognition performance compared with the previous introduced recognition system.

• Journal of Digital Convergence
• /
• v.10 no.10
• /
• pp.215-220
• /
• 2012

This paper proposes a ontology of tree structure approach for adaptive object recognition in a situation-variant environment. In this paper, we introduce a new concept, ontology of tree structure ontology, for context sensitivity, as we found that many developed systems work in a context-invariant environment. Due to the effects of illumination on a supreme obstinate designing context-sensitive recognition system, we have focused on designing such a context-variant system using ontology of tree structure. Ontology can be defined as an explicit specification of conceptualization of a domain typically captured in an abstract model of how people think about things in the domain. People produce ontologies to understand and explain underlying principles and environmental factors. In this research, we have proposed context ontology, context modeling, context adaptation, and context categorization to design ontology of tree structure based on illumination criteria. After selecting the proper light-ontology domain, we benefit from selecting a set of actions that produces better performance on that domain. We have carried out extensive experiments on these concepts in the area of object recognition in a dynamic changing environment, and we have achieved enormous success, which will enable us to proceed on our basic concepts.