• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.6
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• pp.683-690
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• 2015

In Rough set, attribute missing values have several problems such as reduct and core estimation. Further, they do not give some discernable pattern for decision tree construction. Now, there are several methods such as substitutions of typical attribute values, assignment of every possible value, event covering, C4.5 and special LEMS algorithm. However, they are mainly substitutions into frequently appearing values or common attribute ones. Thus, decision rules with high information loss are derived in case that important attribute values are missing in pattern matching. In particular, there is difficult to implement cross validation of the decision rules. In this paper we suggest new method for substituting the missing attribute values into high information gain by using entropy variation among given attributes, and thereby completing the information table. The suggested method is validated by conducting the same rough set analysis on the incomplete information system using the software ROSE.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.161-169
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• 2018

This study presents a time synchronization system in decentralized structure by using the blockchain, a core technology of Bitcoin introduced by Satoshi Nakamoto in 2008. In this study, Getting away from existing time synchronization system in centralized structure, A blockchain network has completely decentralized structure using public blockchain. In decentralized structure, Only certain peers among the peers that participate in a blockchain network access the NTP server. Therefore, others can synchronize time without having to go to public network. Furthermore if appropriate time synchronization cycles are established for each peer, time synchronization can be maintained even when connection to public network is completely lost. A time synchronization system in this study has advantages of p2p system and can be also guaranteed reliability and stability because it used digital signature, merkle tree, consensus algorithm which are core characteristics of block chains.

• The KIPS Transactions:PartC
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• v.12C no.7 s.103
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• pp.1007-1014
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• 2005

A generation of rules or patterns for detecting attacks from network is very difficult. Detection rules and patterns are usually generated by Expert's experiences that consume many man-power, management expense, time and so on. This paper proposes statistical methods that effectively detect intrusion and attacks without expert's experiences. The methods are to select useful measures in measures of network connection(session) and to detect attacks. We extracted the network session data of normal and each attack, and selected useful measures for detecting attacks using relative entropy. And we made probability patterns, and detected attacks using likelihood ratio testing. The detecting method controled detection rate and false positive rate using threshold. We evaluated the performance of the proposed method using KDD CUP 99 Data set. This paper shows the results that are to compare the proposed method and detection rules of decision tree algorithm. So we can know that the proposed methods are useful for detecting Intrusion and attacks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8C
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• pp.522-528
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• 2011

The iterative detection and decoding (IDD) has been shown to dramatically improve the bit error rate (BER) performance of the multiple-input multiple-output (MIMO) communication systems. However, these techniques require a high computational complexity since it is required to compute the soft decisions for each bit. In this paper, we show IDD comprised of sphere decoder with low-density parity check (LDPC) codes and present the tree search strategy, called a layer symbol search (LSS), to obtain soft decisions with a low computational complexity. In addition, an adaptive early termination is proposed to reduce the computational complexity during an iteration between an inner sphere decoder and an outer LDPC decoder. It is shown that the proposed approach can achieve the performance similar to an existing algorithm with 70% lower computational complexity compared to the conventional algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.11A
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• pp.1197-1205
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• 2007

An adaptive K-best detection scheme is proposed for MIMO systems. The proposed scheme changes the number of survivor paths, K based on the degree of the reliability of Zero-Forcing (ZF) estimates at each K-best step. The critical drawback of the fixed K-best detection is that the correct path's metric may be temporarily larger than K minimum paths metrics due to imperfect interference cancellation by the incorrect ZF estimates. Based on the observation that there are insignificant differences among path metrics (ML distances) when the ZF estimates are incorrect, we use the ratio of the minimum ML distance to the second minimum as a reliability indicator for the ZF estimates. So, we adaptively select the value of K according to the ML distance ratio. It is shown that the proposed scheme achieves the significant improvement over the conventional fixed K-best scheme. The proposed scheme effectively achieves the performance of large K-best system while maintaining the overall average computation complexity much smaller than that of large K system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.789-792
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• 2004

In various fields as web mining, bioinformatics, statistical data analysis, and so forth, very diversely missing values are found. These values make training data to be sparse. Largely, the missing values are replaced by predicted values using mean and mode. We can used the advanced missing value imputation methods as conditional mean, tree method, and Markov Chain Monte Carlo algorithm. But general imputation models have the property that their predictive accuracy is decreased according to increase the ratio of missing in training data. Moreover the number of available imputations is limited by increasing missing ratio. To settle this problem, we proposed statistical learning theory to preprocess for missing values. Our statistical learning theory is the support vector regression by Vapnik. The proposed method can be applied to sparsely training data. We verified the performance of our model using the data sets from UCI machine learning repository.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.192-198
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• 2016

Motion planning problem is still one of the important issues in robotic applications. In many real-time motion planning problems, it is advisable to find a feasible solution quickly and improve the found solution toward the optimal one before the previously-arranged motion plan ends. For such reasons, sampling-based approaches are becoming popular for real-time application. Especially the use of a rapidly exploring random $tree^*$ ($RRT^*$) algorithm is attractive in real-time application, because it is possible to approach an optimal solution by iterating itself. This paper presents a modified version of informed $RRT^*$ which is an extended version of $RRT^*$ to increase the rate of convergence to optimal solution by improving the sampling method of $RRT^*$. In online motion planning, the robot plans a path while simultaneously moving along the planned path. Therefore, the part of the path near the robot is less likely to be sampled extensively. For a better solution in online motion planning, we modified the sampling method of informed $RRT^*$ by combining with the sampling method to improve the path nearby robot. With comparison among basic $RRT^*$, informed $RRT^*$ and the proposed $RRT^*$ in online motion planning, the proposed $RRT^*$ showed the best result by representing the closest solution to optimum.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.67-74
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• 2006

Contort-aware computing technology is one of the key technology of ubiquitous computing in the mobile device environment. Context recognition computing enables computer applications that automatically respond to user's everyday activity to be realized. In this paper, We use accelerometer could sense activity states of the object and apply to mobile devices. This method for estimating human motion states utilizes various statistics of accelerometer data, such as mean, standard variation, and skewness, as features for classification, and is expected to be more effective than other existing methods that rely on only a few simple statistics. Classification algorithm uses simple decision tree instead of existing neural network by considering mobile devices with limited resources. A series of experiments for testing the effectiveness of the our context detection system for mobile applications and ubiquitous computing has been performed, and its result is presented.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.338-345
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• 2020

Versatile Video Coding (VVC), which has been developing as a next generation video coding standard, has adopted various techniques to achieve more than twice the compression performance of HEVC (High Efficiency Video Coding). The recently released VVC Test Model (VTM) shows 38% Bjontegaard Delta bitrate (BD-rate) improvement and 9x/1.6x encoding/decoding complexity over HEVC. In order to reduce such increased complexity, various fast algorithms have been proposed. In this paper, gradient-based methods of fast intra mode decision and block splitting are presented. Experimental results show that, compared to VTM6.0, the proposed method gives up to 65% encoding time reduction with 3.54% BD-rate loss in All-Intra (AI) configuration.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.4
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• pp.13-29
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• 2020

Automobiles have long been an essential part of daily life, but the social costs of car traffic accidents exceed 9% of the national budget of Korea. Hence, it is necessary to establish prevention and response system for car traffic accidents. In order to present a model that can classify and predict the degree of injury in car traffic accidents, we used big data analysis techniques of K-nearest neighbor, logistic regression analysis, naive bayes classifier, decision tree, and ensemble algorithm. The performances of the models were analyzed by using the data on the nationwide traffic accidents over the past three years. In particular, considering the difference in the number of data among the respective injury severity levels, we used down-sampling methods for the group with a large number of samples to enhance the accuracy of the classification of the models and then verified the statistical significance of the models using ANOVA.