• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.1010-1018
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• 2009

An inertial navigation system for pedestrian position tracking is proposed, where the position is computed using inertial sensors mounted on shoes. Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it needs to reset errors frequently. During normal walking, there is an almost periodic zero velocity instance when a foot touches the floor. Using this fact, estimation errors are reduced and this method is called the zero velocity updating algorithm. When implementing this zero velocity updating algorithm, it is important to know when is the zero velocity interval. The gait states are modeled as a Markov process and each state is estimated using the hidden Markov model smoother. With this gait estimation, the zero or nearly zero velocity interval is more accurately estimated, which helps to reduce the position estimation error.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.281-290
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• 2013

It is well known that the DD-plot is a useful graphical tool for non-parametric classification. In this paper, we propose another use of DD-plot for detecting the out-of-control state in multivariate process. We suggested a dynamic version of DD-plot and its accompanying a quality index plot in such case.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1554-1574
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• 2017

This paper introduces a new type of determining factor for Pseudo Random Strings (PRS). This classification depends upon a mathematical property called Finite Induction (FI). FI is similar to a Markov Model in that it presents a model of the sequence under consideration and determines the generating rules for this sequence. If these rules obey certain criteria, then we call the sequence generating these rules FI a PRS. We also consider the relationship of these kinds of PRS's to Good/deBruijn graphs and Linear Feedback Shift Registers (LFSR). We show that binary sequences from these special graphs have the FI property. We also show how such FI PRS's can be generated without consideration of the Hamiltonian cycles of the Good/deBruijn graphs. The FI PRS's also have maximum Shannon entropy, while sequences from LFSR's do not, nor are such sequences FI random.

• Journal of the Korea Computer Industry Society
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• v.9 no.2
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• pp.93-98
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• 2008

In this study, analyzed principle about stream cipher that is formed to Cellular Automata foundation. Cellular Automata can embody complicated and various principle with simple identifying marks that is State, Neighborhood, Transition Rules originally. Cellular Automata is hinting that can handle encipherment smoothly using transition rule. Create binary pad (key stream) by Cellular Automata's transition rule 30 applications in treatise that see therefore, and experimented ability of encryption and decryption because using stream cipher of symmetric key encryption way of password classification.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.2
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• pp.109-117
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• 2022

This paper proposes a method for detecting flight anomalies of drones through the difference between the command of flight controller (FC) and the navigation solution. If the drones make a flight normally, control errors generated by the difference between the desired control command of FC and the navigation solution should converge to zero. However, there is a risk of sudden change or divergence of control errors when the FC control feedback loop preset for the normal flight encounters interferences such as strong winds or navigation sensor abnormalities. In this paper, we propose the method with a deep neural network model that predicts the control error in the normal flight so that the abnormal flight state can be detected. The performance of proposed method was evaluated using the real-world flight data. The results showed that the method effectively detects anomalies in various situation.

• Asia pacific journal of information systems
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• v.28 no.4
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• pp.308-319
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• 2018

This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark ML, which are traditional systems and Big Data respectively. Experimenting with sample dataset in Azure, we found that the Decision Forest model is the most accurate to proceed in terms of the recall value. For the massive data set using Spark ML, it is found that the Random Forest classifier algorithm of the classification model proves to be the best algorithm. It is presented that the Spark cluster gets much faster to build and evaluate models as adding more servers to the cluster with the same accuracy, which proves that the large scale data set can be predictable using Big Data platform. Finally, we reached a recall score with 0.73, which implies a satisfying prediction quality in predicting fraudulent transactions.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.3
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• pp.274-279
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• 2015

The main goal of this research is to build a sentiment analysis system which automatically determines user opinions of the Stanford Sentiment Treebank in terms of three sentiments such as positive, negative, and neutral. Firstly, sentiment sentences are POS tagged and parsed to dependency structures. All nodes of the Treebank and their polarities are automatically extracted from the Treebank. We train two Support Vector Machines models. One is for a node level classification and the other is for a sentence level. We have tried various type of features such as word lexicons, POS tags, Sentiment lexicons, head-modifier relations, and sibling relations. Though we acquired 74.2% in accuracy on the test set for 3 class node level classification and 67.0% for 3 class sentence level classification, our experimental results for 2 class classification are comparable to those of the state of art system using the same corpus.

• Journal of the Korean Society for information Management
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• v.22 no.3 s.57
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• pp.261-287
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• 2005

The purpose of this paper is to explore the ways to improve the performance of SVM (Support Vector Machines) text classifier using inter-document similarities. SVMs are powerful machine learning systems, which are considered as the state-of-the-art technique for automatic document classification. In this paper text categorization via SVMs approach based on feature representation with document vectors is suggested. In this approach, document vectors instead of index terms are used as features, and vector similarities instead of term weights are used as feature values. Experiments show that SVM classifier with document vector features can improve the document classification performance. For the sake of run-time efficiency, two methods are developed: One is to select document vector features, and the other is to use category centroid vector features instead. Experiments on these two methods show that we can get improved performance with small vector feature set than the performance of conventional methods with index term features.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.473-482
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• 2007

Network-based IDS(Intrusion Detection System) gathers network packet data and analyzes them into attack or normal. They raise alarm when possible intrusion happens. But they often output a large amount of low-level of incomplete alert information. Consequently, a large amount of incomplete alert information that can be unmanageable and also be mixed with false alerts can prevent intrusion response systems and security administrator from adequately understanding and analyzing the state of network security, and initiating appropriate response in a timely fashion. So it is important for the security administrator to reduce the redundancy of alerts, integrate and correlate security alerts, construct attack scenarios and present high-level aggregated information. False alarm rate is the ratio between the number of normal connections that are incorrectly misclassified as attacks and the total number of normal connections. In this paper we propose a false alarm classification model to reduce the false alarm rate using classification analysis of data mining techniques. The proposed model can classify the alarms from the intrusion detection systems into false alert or true attack. Our approach is useful to reduce false alerts and to improve the detection rate of network-based intrusion detection systems.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.297-302
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• 2004

In this paper, the 3-D shape description for the objects with the cone ridge and valley surfaces, and the corresponding threshold value selection for surface classification are considered. The existing method based on the mean and Gaussian curvatures(H and K) of differential geometries cannot properly describe cone primitives, which are some of the most common objects in the real world. Also the existing method for surface classification based on the sign values of H and K has Problems in practical applications. For this, cone surface shapes are classified cone ridges and cone valleys are derived from surfaces using the fact that H values are constant case of cylinder surfaces and variable for cone surfaces, respectively. Also threshold value selection for surface classification from a statistical point of view is proposed. The effectiveness of the proposed methods are verified through experiments.