• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.591-605
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• 2022

As drones are widely used, attack attempts using drone vulnerabilities are increasing, and drone security is growing in importance. This paper derives security requirements for reconnaissance drone delivered to government office through threat modeling. Threats are analyzed by the data flow of the drone and collecting possible vulnerabilities. Attack tree is built by analyzed threats. The security requirements were derived from the attack tree and compared with the security requirements suggested by national organizations. Utilizing the security requirements derived from this paper will help in the development and evaluation of secure drones.

• Journal of Forest and Environmental Science
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• v.39 no.2
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• pp.65-72
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• 2023

The endangered species of Cyathea spinulosa (tree ferns) are among the least concerned ferns of Nepal that bring threats to them and their habitat. A way to reduce such threats is by maintaining a database of species' whereabouts and generating a scientific understanding the habitat preferences. This will eventually help in the formulation of conservation plans for the species. This research aimed to characterize the suitable habitat of C. spinulosa by enumerating the location of species in the Panchase Forests of central Nepal. The statistical index method was applied to relate the occurrence locations of species with various environmental factors for the development of indices. The suitable habitat of C. spinulosa (more and most suitable categories) covered 119 km² and accounted for 43% of the total area studied. 74.4% of occurrence locations of C. spinulosa were recorded from these habitats. The habitat characteristics suitable for C. spinulosa were: proximity to streams (high moisture), land covered by forested area (shady area), mid-elevations of hills about 1,000 m to 2,000 m (sub-tropical climate), slope gradient of 20° to 40° (steep slopes), and northern to eastern aspects. These habitat characteristics could be considered for in-situ protection of tree ferns and designating the conservation plots.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.558-566
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• 2005

We propose a feature-based multi-resolution representation of B-rep solid models using history-based Boolean operations based on the merge-and-select algorithm. Because union and subtraction are commutative in the history-based Boolean operations, the integrity of the models at various levels of detail (LOD) is guaranteed for the reordered features regardless of whether the features are subtractive or additive. The multi-resolution solid representation proposed in this paper includes a non-manifold topological merged-set model of all feature primitives as well as a feature-modeling tree reordered consistently with a given LOD criterion. As a result, a B-rep solid model for a given LOD can be provided quickly, because the boundary of the model is evaluated without any geometric calculation and extracted from the merged set by selecting the entities contributing to the LOD model shape.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.6
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• pp.206-219
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• 1996

This paper describes a data structure and evaluation algorithm to improve the perofmrances MOS logic-with-timing simulation in computation and accuracy. In order to efficiently simulate the logic and timing of driver-load networks, (1) a tree data structure to represent the mutual interconnection topology of switches and nodes in the driver-lod network, and (2) an algebraic modeling to efficiently deal with the new represetnation, (3) an evaluation algorithm to compute the linear resistive and capacitive behavior with the new modeling of driver-load networks are developed. The higher modeling presented here supports the structural and functional compatibility with the linear switch-level to simulate the logic-with-timing of digital MOS circuits at a mixed-level. This research attempts to integrate the new approach into the existing simulator RSIM, which yield a mixed-klevel logic-with-timing simulator MIXIM. The experimental results show that (1) MIXIM is a far superior to RSIM in computation speed and timing accuracy; and notably (2) th etiming simulation for driver-load netowrks produces the accuracy ranged within 17% with respect ot the analog simulator SPICE.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.2
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• pp.76-81
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• 2017

In this paper, the risk analysis of smart home services was implemented by applying threat modeling. Identified possible threats for safe deployment of smart home services and identified threats through the STRIDE model. Through the creation of the Attack Tree, the attackable risk was analyzed and the risk was measured by applying the DREAD model. The derived results can be used to protect assets and mitigate risk by preventing security vulnerabilities from compromising and identifying threats from adversely affecting services. In addition, the modeled result of the derived threat can be utilized as a basis for performing the security check of the smart home service.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.17-25
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• 2001

Tree model is the most popular classification algorithm in data mining due to easy interpretation of the result. In CART(Breiman et al., 1984) and C4.5(Quinlan, 1993) which are representative of tree algorithms, the split fur classification proceeds to attain the homogeneous terminal nodes with respect to the composition of levels in target variable. But, fur instance, in the chum prediction modeling fur CRM(Customer Relationship management), the rate of churn is generally very low although we are interested in mining the churners. Thus it is difficult to get accurate prediction modes using tree model based on the traditional split rule, such as mini or deviance. Buja and Lee(1999) introduced a new split rule, one-sided purity for classifying minor interesting group. In this paper, we compared one-sided purity with traditional split rule, deviance analyzing churning vs. non-churning data of ISP company. Also reviewing the result of tree model based on one-sided purity with some simulated data, we discussed problems and researchable topics.

• Environmental Engineering Research
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• v.21 no.2
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• pp.152-163
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• 2016

Present study investigates the potential of cassava peel and rubber tree bark for the removal of Cr (VI) from aqueous solution. Removal efficiency of more than 99% was obtained during the kinetic adsorption experiments with dosage of 3.5 g/L for cassava peel and 8 g/L for rubber tree bark. By comparing popular isotherm models and kinetic models for evaluating the kinetics of mass transfer, it was observed that Redlich-Peterson model and Langmuir model fitted well ($R^2$ > 0.99) resulting in maximum adsorption capacity as 79.37 mg/g and 43.86 mg/g for cassava peel and rubber tree bark respectively. Validation of pseudo-second order model and Elovich model indicated the possibility of chemisorption being the rate limiting step. The multi-linearity in the diffusion model was further addressed using multi-sites models (two-site series interface (TSSI) and two-site parallel interface (TSPI) models). Considering the influence of interface properties on the kinetic nature of sorption, TSSI model resulted in low mass transfer rate (5% for cassava peel and 10% for rubber tree bark) compared to TSPI model. The study highlights the employability of two-site sorption model for simultaneous representation of different stages of kinetic sorption for finding the rate-limiting process, compared to the separate equilibrium and kinetic modeling attempts.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.41-47
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• 2008

Cluster based fuzzy model tree has certain drawbacks to decrease performance of testinB data when over-fitting of training data exists. To reduce the sensitivity of performance due to over-fitting problem, we proposed a modified cluster based fuzzy model tree with node information. To construct model tree, cluster centers are calculated by fuzzy clustering method using all input and output attributes in advance. And then, linear models are constructed at internal nodes with fuzzy membership values between centers and input attributes. In the prediction step, membership values are calculated by using fuzzy distance between input attributes and all centers that passing the nodes from root to leaf nodes. Finally, data prediction is performed by the weighted average method with the linear models and fuzzy membership values. To show the effectiveness of the proposed method, we have applied our method to various dataset. Under various experiments, our proposed method shows better performance than conventional cluster based fuzzy model tree.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.615-627
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• 2009

In micro marketing promotion, it is important to know the behavior of customers. In this study we are interested in the forecasting of repurchase of customers from customers' behavior. By analyzing the cosmetic transaction data we derive some variables which play an important role in the knowledge of the customers' behavior and in the modeling of repurchase. As modeling tools we use the decision tree, logistic regression and neural network model. Finally we decide to use the decision tree as a final model since it yields the smallest RASE (root average squared error) and the greatest correct classification rate.