• Convergence Security Journal
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• v.13 no.1
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• pp.71-78
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• 2013

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The doubly bounded power law distribution model makeup Weibull distribution applied to distribution was based on finite failure NHPP. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than automatic error that is generally efficient model could be confirmed. This paper, a numerical example of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and $R^2$.

• Convergence Security Journal
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• v.11 no.1
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• pp.25-32
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• 2011

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The Weibull distribution applied to distribution was based on finite failure NHPP. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than automatic error that is generally efficient model could be confirmed. This paper, a numerical example of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and $R_{sq}$.

• Journal of Navigation and Port Research
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• v.31 no.10
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• pp.853-858
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• 2007

In this paper, a seesaw system composed with a moving cart on the rail and seesaw frame is made to demonstrate the effectiveness of the control theory. The control aim is to maintain an equilibrium of seesaw frame in spite of various initial conditions and an allowable disturbance. To solve this control problem, a PI-type state feedback controller using reduced-order observer is implemented and applied to the seesaw system. The reduced-order observer can be used to estimate the state variables in the case of the limit of sensor number or the constraint on setting sensors and the cost. A series of simulation are carried out to verify the effectiveness of the control system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1344-1356
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• 2009

The DDS (Data Distribution Service) is a data-centric communication technology that provides an efficient communication service that supports a dynamic plug & play through an automatic setting of participants' location information for each data (Topic) by using DDS discovery technique. This paper proposes the hierarchical-structured DDS discovery technique (SPDP-TBF) suitable for the large-scale distributed systems by comparing and analyzing the existing DDS discovery techniques in terms of performance and problem areas. The proposed SPDP-TBF performs the periodic discovery of the involved participants only by having separate hierarchical managers which take charge of the registration and search (of participants) so that a participant sends its information to the related participants only, and it enhances the effectiveness of the message transfer. Moreover, the proposed SPDP-TBF provides the improved scalability by performing the hierarchical discovery through hierarchical manager nodes so that it can be applied to the large-scale distributed system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1604-1611
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• 2007

In a process of insect footprint recognition, basic footprint segments should be extracted from a whole insect footprint image in order to find out appropriate features for classification. In this paper, we used a clustering method as a preprocessing stage for extraction of basic insect footprint segments. In general, sizes and strides of footprints may be different according to type and sire of an insect for recognition. Therefore we proposed an improved ART2 algorithm for extraction or basic insect footprint segments regardless of size and stride or footprint pattern. In the proposed ART2 algorithm, threshold value for clustering is determined automatically using contour shape of the graph created by accumulating distances between all the spots of footprint pattern. In the experimental results applying the proposed method to two kinds of insect footprint patterns, we could see that all the clustering results were accomplished correctly.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.53-58
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• 2018

MANET transmits data by hop-by-hop method because it is composed of mobile nodes without support of any infrastructure. Its structure is very similar to a block chain. However, it is exposed to various threats such as data tampering or destruction by malicious nodes because of transmission method. So, ensuring the integrity of transmitted data is an important complement to MANET. In this paper, we propose a method to apply the block chain technique in order to protect the reliability value of the nodes consisting the network from malicious nodes. For this, hierarchical structure of a cluster type is used. Only cluster head stores the reliability information of the nodes in a block and then, this can be spread. In addition, we applied block generation difficulty automatic setting technique using the number of nodes selecting cluster head and the reliability of cluster head to prevent the spread of wrong blocks. This can prevent block generation and spread by malicious nodes. The superior performance of the proposed technique can be verified by comparing experiments with the SAODV technique.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.11
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• pp.69-78
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• 2019

The purpose of this study is to propose a modeling methodology through the exchange of coordinate data of a three-dimensional custom curtain wall panel between Rhino and Revit, and to examine the validity of the model implemented in the drawing. Although the modeling means and method are different, a fundamental principle is that all three-dimensional modeling begins by defining the position of the points, the most primitive element of geometry, in the XYZ coordinate space. For the BIM modeling methodology proposal based on this geometry basic concept, the functions and characteristics associated with the points of Rhino and Revit programs are identified, and then BIM implementation process model is organized and systemized through the setting of the interoperability process algorithm. The BIM implementation process model proposed in this study is (1) Modeling and panelizing surface into individual panels using Rhino and Grasshopper; (2) Extraction of vertex coordinate data from individual panels and create CSV file; (3) Curtain wall modeling through Adaptive Component Family in Revit and (4) Automatic creation of Revit curtain wall panels through API. The proposed process model is expected to help reduce design errors and improve component and construction quality by automatically converting general elements into architectural meaningful information, automating a set of processes that build them into BIM data, and enabling consistent and integrated design management.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.71-79
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• 2020

In this paper, we propose a theft prevention technology based on beacon and smart device for smart stroller. The smart stroller has two Bluetooth devices. One is for data transmission and the other is for distance measurement as beacon. Thus, smart device uses a Bluetooth for strength level setting, manual locking and data transmission of smart stroller. Furthermore, smart device can do automatic locking of smart stroller using Bluetooth beacon when the stroller go further away from smart device. At this time, we apply a distance measurement algorithm and theft prevention algorithm with improved beacon distance measurement technique. To show usefulness of the proposed technology, we make a smart stroller and developed a smart device application. We did two distance measurement experiments and a theft prevention experiment with the proposed technology and the result shows 91.3% accuracy for theft prevention. Therefore, the proposed technology would be more usefulness technology for smart stroller.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.62-69
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• 2022

An electrocardiogram (ECG) is one of the most important biosignals, and in particular, continuous ECG monitoring is very important in patients with arrhythmia. There are many different types of arrhythmia (sinus node, sinus tachycardia, atrial premature beat (APB), and ventricular fibrillation) depending on the cause, and continuous ECG monitoring during daily life is very important for early diagnosis of arrhythmias and setting treatment directions. The ECG signal of arrhythmia patients is very unstable, and it is difficult to detect the R-peak point, which is a key feature for automatic arrhythmias detection. In this study, we develped a continuous measuring Holter ECG monitoring device and software for analysis and confirmed the utility of R-peak of the ECG signal with MIT-BIH arrhythmia database. In future studies, it needs the validation of algorithms and clinical data for morphological classification and prediction of arrhythmias due to various etiologies.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.346-353
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• 2023

In this paper, we propose an innovative approach that leverages deep learning to find optimal reference points for achieving precise tooth segmentation in three-dimensional tooth point cloud data. A dataset consisting of 350 aligned maxillary and mandibular cloud data was used as input, and both end coordinates of individual teeth were used as correct answers. A two-dimensional image was created by projecting the rendered point cloud data along the Z-axis, where an image of individual teeth was created using an object detection algorithm. The proposed algorithm is designed by adding various modules to the Unet model that allow effective learning of a narrow range, and detects both end points of the tooth using the generated tooth image. In the evaluation using DSC, Euclid distance, and MAE as indicators, we achieved superior performance compared to other Unet-based models. In future research, we will develop an algorithm to find the reference point of the point cloud by back-projecting the reference point detected in the image in three dimensions, and based on this, we will develop an algorithm to divide the teeth individually in the point cloud through image processing techniques.