• Proceedings of the Korea Electromagnetic Engineering Society Conference
• /
• 2005.11a
• /
• pp.163-168
• /
• 2005

Attenuation in vegetation is important, for both terrestrial and earth-space systems. However, the wide range of conditions and types of foliage makes it difficult to develop a generalized prediction procedure. Currently, there is also a lack of suitably prediction model and measured experimental data for vegetation loss. So in this paper, vegetation loss data for four different tree-species, including Dawn-redwood tree, Plane tree, Pine tree and Fir tree are obtained by measurement in the frequency range of 1.0 $\sim$ 6.0 GHz. The through or scattered component is calculated using a model based upon the theory of RET(Radiative Energy Transfer) and RET modeling parameters are extracted from the measured data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.8 no.4
• /
• pp.327-337
• /
• 2015

This work provides new forensic techinque to a hide message on a directory index in Windows NTFS file system. Behavior characteristics of B-tree, which is apoted to manage an index entry, is utilized for hiding message in slack space of an index record. For hidden message not to be exposured, we use a disguised file in order not to be left in a file name attribute of a MFT entry. To understand of key idea of the proposed technique, we describe B-tree indexing method and the proposed of this work. We show the proposed technique is practical for anti-forensic usage with a real message hiding case using a developed software tool.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.10 no.5
• /
• pp.57-62
• /
• 2010

Recently, decision tree techniques have been studied in terms of quick searching and extracting of massive data in medical fields. Although many different techniques have been developed such as CART, C4.5 and CHAID which are belong to a pie in Clermont decision tree classification algorithm, those methods can jeopardize remained data by the binary method during procedures. In brief, C4.5 method composes a decision tree by entropy levels. In contrast, CART method does by entropy matrix in categorical or continuous data. Therefore, we compared C4.5 and CART methods which were belong to a same pie using breast cancer data to evaluate their performance respectively. To convince data accuracy, we performed cross-validation of results in this paper.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.3
• /
• pp.107-114
• /
• 2017

In beacon-enabled mode, beacon collision is a significant problem for the scalability of cluster-tree wireless sensor networks. In this paper, multi-channel time division scheduling (MCTS) is proposed to prevent beacon collisions and provide scalability. A coordinator broadcasts a beacon frame, including information on allocated channels and time-slots, and a new node determines its own channel and time-slot. The performance of the proposed method is evaluated by comparing the proposed approach with a typical ZigBee. MCTS prevents beacon collisions in cluster-tree wireless sensor networks. It enables large-scale wireless sensor networks based on a cluster tree to be scalable and effectively constructed.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.2
• /
• pp.127-133
• /
• 2019

Since frequent item extraction so far requires searching for patterns and traversal for the FP-Tree, it is more likely to store the mining data in a tree and thus CPU time is required for its searching. In order to overcome these drawbacks, in this paper, we provide each item with its location identification of transaction data without relying on conditional FP-Tree and convert transaction data into 2-dimensional position information look-up table, resulting in the facilitation of time and spatial accessibility. We propose an algorithm that considers the mapping scheme between the location of items and items that guarantees the linear time complexity. Experimental results show that the proposed method can reduce many execution time and memory usage based on the data set obtained from the FIMI repository website.

• International Journal of Computer Science & Network Security
• /
• v.24 no.2
• /
• pp.150-154
• /
• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• ETRI Journal
• /
• v.28 no.4
• /
• pp.506-508
• /
• 2006

A frequency selective surface (FSS), whose unit cell consists of a ternary tree loop loaded with a modified tripole, is proposed to block multiple frequency bands. Target frequency bands correspond to Korean personal communication services, cellular mobile communication, and 2.4 GHz industrial, scientific, and medical bands. Through the adjustment of inter-element and inter-unit cell gaps, and adjustment of the length of elements, we present an FSS design method that makes the precise tuning of multiple resonance frequencies possible. Additionally, to verify the validity of our approach, simulation results obtained from a commercial software tool and experimental data are also presented.

• International journal of advanced smart convergence
• /
• v.1 no.1
• /
• pp.57-60
• /
• 2012

In this paper, we have performed a safety analysis on an automotive evaporator inspection system. We performed the bi-directional analysis on the manufacturing line. Software Fault Tree Analysis (SFTA) as backward analysis and Software Failure Modes, Effects, & Criticality Analysis (SFMECA) as forward analysis are performed alternately to detect potential cause-to-effect relations. The analysis results indicate the possibility of searching and summarizing fault patterns for future reusability.

• Journal of Broadcast Engineering
• /
• v.27 no.3
• /
• pp.361-368
• /
• 2022

Quaternary tree plus multi-type tree (QT+MTT) structure was adopted in the Versatile Video Coding (VVC) standard as a block partitioning tool. QT+MTT provides excellent coding gain; however, it has huge encoding complexity due to the flexibility of the binary tree (BT) and ternary tree (TT) splits. This paper proposes a fast inter coding unit (CU) partitioning algorithm for BT and TT split types based on prediction accuracy functions using the mean of the absolute error (MAE). The MAE-based decision model was established to achieve a consistent time-saving encoding with stable coding loss for a practical low complexity VVC encoder. Experimental results under random access test configuration showed that the proposed algorithm achieved the encoding time saving from 24.0% to 31.7% with increasing luminance Bjontegaard delta (BD) rate from 1.0% to 2.1%.

• Journal of IKEEE
• /
• v.22 no.3
• /
• pp.850-853
• /
• 2018

In this paper, we propose a decoding method using a balanced binary tree and a canonical Huffman tree for efficient decoding of Huffman codes. The balanced binary tree scheme reduces the number of searches by lowering the height of the tree and binary search. However, constructing a tree based on the value of the code instead of frequency of symbol is a drawback of the balanced binary tree. In order to overcome these drawbacks, a balanced binary tree is reconstructed according to the occurrence probability of symbols at each level of the tree and binary search is performed for each level. We minimize the number of searches using a canonical Huffman tree to find level of code to avoid searching sequentially from the top level to bottom level.