• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.3
• /
• pp.230-245
• /
• 2005

In this paper, we develop a gesture-based input device equipped with accelerometers and gyroscopes. The sensors measure the inertial measurements, i.e., accelerations and angular velocities produced by the movement of the system when a user is inputting gestures on a plane surface or in a 3D space. The gyroscope measurements are integrated to give orientation of the device and consequently used to compensate the accelerations. The compensated accelerations are doubly integrated to yield the position of the device. With this approach, a user's gesture input trajectories can be recovered without any external sensors. Three versions of motion tracking algorithms are provided to cope with wide spectrum of applications. Then, a Bayesian network based recognition system processes the recovered trajectories to identify the gesture class. Experimental results convincingly show the feasibility and effectiveness of the proposed gesture input device. In order to show practical use of the proposed input method, we implemented a prototype system, which is a gesture-based remote controller (Magic Wand).

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.1
• /
• pp.86-99
• /
• 2011

Due to the uncertainty of intention recognition for behaviors of users, the intention is differently recognized according to the situation for the same behavior by the same user, the accuracy of user intention recognition by minimizing the uncertainty is able to be improved. This paper suggests a novel ontology-based method to recognize user intentions, and able to minimize the uncertainties that are the obstacles against the precise recognition of user intention. This approach creates ontology for user intention, makes a hierarchy and relationship among user intentions by using RuleML as well as Dynamic Bayesian Network, and improves the accuracy of user intention recognition by using the defined RuleML as well as the gathered sensor data such as temperature, humidity, vision, and auditory. To evaluate the performance of robot proactive planning mechanism, we developed a simulator, carried out some experiments to measure the accuracy of user intention recognition for all possible situations, and analyzed and detailed described the results. The result of our experiments represented relatively high level the accuracy of user intention recognition. On the other hand, the result of experiments tells us the fact that the actions including the uncertainty get in the way the precise user intention recognition.

• Journal of KIISE:Databases
• /
• v.33 no.2
• /
• pp.163-174
• /
• 2006

In distributed wireless sensor network, it is difficult to transmit and analyze the entire stream data depending on limited networks, power and processor. Therefore it is suitable to use alternative stream data processing after classifying the continuous stream data. We propose a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes input as a sliding window of multivariate stream data and discretizes the data in the window into a string of symbols that characterize the signal changes. In the classification step, it uses a standard text classification algorithm to classify the discretized data in the window. We evaluated both supervised and unsupervised classification algorithms. For supervised, we tested Bayesian classifier and SVM, and for unsupervised, we tested Jaccard, TFIDF Jaro and Jaro Winkler. In our experiments, SVM and TFIDF outperformed other classification methods. In particular, we observed that classification accuracy is improved when the correlation of attributes is also considered along with the n-gram tokens of symbols.

• Safety and Health at Work
• /
• v.9 no.1
• /
• pp.42-52
• /
• 2018

Background: Maintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high man-machine interactions in challenging and evolving conditions. The evolving conditions are weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress. For example, extreme weather condition affects seafarers' performance, increasing the chances of error, and, consequently, can cause injuries or fatalities to personnel. An effective human error probability model is required to better manage maintenance on-board ships. The developed model would assist in developing and maintaining effective risk management protocols. Thus, the objective of this study is to develop a human error probability model considering various internal and external factors affecting seafarers' performance. Methods: The human error probability model is developed using probability theory applied to Bayesian network. The model is tested using the data received through the developed questionnaire survey of >200 experienced seafarers with >5 years of experience. The model developed in this study is used to find out the reliability of human performance on particular maintenance activities. Results: The developed methodology is tested on the maintenance of marine engine's cooling water pump for engine department and anchor windlass for deck department. In the considered case studies, human error probabilities are estimated in various scenarios and the results are compared between the scenarios and the different seafarer categories. The results of the case studies for both departments are also compared. Conclusion: The developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when new information is available on changes in either internal (i.e., training, experience, and fatigue) or external (i.e., environmental and operational conditions such as weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress) factors.

• Convergence Security Journal
• /
• v.19 no.1
• /
• pp.11-17
• /
• 2019

Recently, the abuse of Internet technology has caused economic and mental harm to society as a whole. Especially, malicious code that is newly created or modified is used as a basic means of various application hacking and cyber security threats by bypassing the existing information protection system. However, research on small-capacity executable files that occupy a large portion of actual malicious code is rather limited. In this paper, we propose a model that can analyze the characteristics of known small capacity executable files by using data mining techniques and to use them for detecting unknown malicious codes. Data mining analysis techniques were performed in various ways such as Naive Bayesian, SVM, decision tree, random forest, artificial neural network, and the accuracy was compared according to the detection level of virustotal. As a result, more than 80% classification accuracy was verified for 34,646 analysis files.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.3
• /
• pp.333-340
• /
• 2019

A combat system performs a given mission enduring various threats. It is important to analyze the reliability of combat systems in order to increase their ability to perform a given mission. Most of studies considered no threat or on threat and didn't analyze all the dependent relationships among the components. In this paper, we analyze the loss probability of the function of the combat system and use it to analyze the reliability. The proposed method is divided into two layers, A lower layer and a upper layer. In lower layer, the failure probability of each components is derived by using FTA to consider various threats. In the upper layer, The loss probability of function is analyzed using the failure probability of the component derived from lower layer and BBN in order to consider the dependent relationships among the components. Using the proposed method, it is possible to analyze considering various threats and the dependency between components.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.10
• /
• pp.1296-1301
• /
• 2021

With the recent increase in diabetes incidence worldwide, research has been conducted to predict diabetes through various machine learning and deep learning technologies. In this work, we present a model for predicting diabetes using machine learning techniques with German Frankfurt Hospital data. We apply outlier handling using Interquartile Range (IQR) techniques and Pearson correlation and compare model-specific diabetes prediction performance with Decision Tree, Random Forest, Knn (k-nearest neighbor), SVM (support vector machine), Bayesian Network, ensemble techniques XGBoost, Voting, and Stacking. As a result of the study, the XGBoost technique showed the best performance with 97% accuracy on top of the various scenarios. Therefore, this study is meaningful in that the model can be used to accurately predict and prevent diabetes prevalent in modern society.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.20 no.4
• /
• pp.63-73
• /
• 2010

Malicious bot programs, the source of distributed denial of service attack, are widespread and the number of PCs which were infected by malicious bot program are increasing geometrically thesedays. The continuous distributed denial of service attacks are happened constantly through these bot PCs and some financial incident cases have found lately. Therefore researches to response distributed denial of service attack are necessary so we propose an effective feature generation method for distributed denial of service attack detection using entropy. In this paper, we apply our method to both the DARPA 2000 datasets and also the distributed denial of service attack datasets that we composed and generated ourself in general university. And then we evaluate how the proposed method is useful through classification using bayesian network classifier.

• Journal of Korean Society on Water Environment
• /
• v.36 no.6
• /
• pp.611-620
• /
• 2020

Although process-based models have been a preferred approach for modeling freshwater aquatic systems over extended time intervals, the increasing utility of data-driven models in a big data environment has made the data-driven models increasingly popular in recent decades. In this study, international peer-reviewed journals for the relevant fields were searched in the Web of Science Core Collection, and an extensive literature review, which included total 2,984 articles published during the last two decades (2000-2020), was performed. The review results indicated that the rate of increase in the number of published studies using data-driven models exceeded those using process-based models since 2010. The increase in the use of data-driven models was partly attributable to the increasing availability of data from new data sources, e.g., remotely sensed hyperspectral or multispectral data. Consistently throughout the past two decades, South Korea has been one of the top ten countries in which the greatest number of studies using the data-driven models were published. Among the major data-driven approaches, i.e., artificial neural network, decision tree, and Bayesian model, were illustrated with case studies. Based on the review, this study aimed to inform the current state of knowledge regarding the biogeochemical water quality and ecological models using data-driven approaches, and provide the remaining challenges and future prospects.

• Journal of The Korean Astronomical Society
• /
• v.53 no.6
• /
• pp.161-168
• /
• 2020

We report the discovery of a giant exoplanet in the microlensing event OGLE-2017-BLG-1049, with a planet-host star mass ratio of q = 9.53 ± 0.39 × 10-3 and a caustic crossing feature in Korea Microlensing Telescope Network (KMTNet) observations. The caustic crossing feature yields an angular Einstein radius of θE = 0.52 ± 0.11 mas. However, the microlens parallax is not measured because the time scale of the event, tE ≃ 29 days, is too short. Thus, we perform a Bayesian analysis to estimate physical quantities of the lens system. We find that the lens system has a star with mass Mh = 0.55+0.36-0.29 M⊙ hosting a giant planet with Mp = 5.53+3.62-2.87 MJup, at a distance of DL = 5.67+1.11-1.52 kpc. The projected star-planet separation is a⊥ = 3.92+1.10-1.32 au. This means that the planet is located beyond the snow line of the host. The relative lens-source proper motion is μrel ~ 7 mas yr-1, thus the lens and source will be separated from each other within 10 years. After this, it will be possible to measure the flux of the host star with 30 meter class telescopes and to determine its mass.