• Journal of Software Engineering Society
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• v.26 no.4
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• pp.93-103
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• 2013

Complexity and maintenance cost of software increase as much as software has been evolved, therefore importance of software maintenance recently arise. There are many signs that are difficulties to maintain software, called bad smell, in a large-scale software. The bad smell should be removed to improve maintainability. Recently, many software refactoring methods have researched to terminate the bad smell. In this paper, we propose how to identify long parameter list, which causes bad smell, and how to solve the problem for increasing software maintainability. In our approach, we classify the parameters for creating new objects by measuring semantic similarity among them. This is evaluated by experienced software developers, and the result is statistically verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1845-1865
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• 2019

Current Internet is designed by lots of service providers with different objects and policies which make the direct deployment of radically new architecture and protocols on Internet nearly impossible without reaching a consensus among almost all of them. Network virtualization is proposed to fend off this ossification of Internet architecture and add diversity to the future Internet. As an important part of network virtualization, virtual network embedding (VNE) problem has received more and more attention. In order to solve the problems of large embedding cost, low acceptance ratio (AR) and environmental adaptability in VNE algorithms, cognitive method is introduced to improve the adaptability to the changing environment and a cognitive virtual network embedding algorithm based on weighted relative entropy (WRE-CVNE) is proposed in this paper. At first, the weighted relative entropy (WRE) method is proposed to select the suitable substrate nodes and paths in VNE. In WRE method, the ranking indicators and their weighting coefficients are selected to calculate the node importance and path importance. It is the basic of the WRE-CVNE. In virtual node embedding stage, the WRE method and breadth first search (BFS) algorithm are both used, and the node proximity is introduced into substrate node ranking to achieve the joint topology awareness. Finally, in virtual link embedding stage, the CPU resource balance degree, bandwidth resource balance degree and path hop counts are taken into account. The path importance is calculated based on the WRE method and the suitable substrate path is selected to reduce the resource fragmentation. Simulation results show that the proposed algorithm can significantly improve AR and the long-term average revenue to cost ratio (LTAR/CR) by adjusting the weighting coefficients in VNE stage according to the network environment. We also analyze the impact of weighting coefficient on the performance of the WRE-CVNE. In addition, the adaptability of the WRE-CVNE is researched in three different scenarios and the effectiveness and efficiency of the WRE-CVNE are demonstrated.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.89-96
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• 2019

360 degree 3-dimensional lidar sensors and vision cameras are commonly used in the development of autonomous driving techniques for automobile, drone, etc. By the way, existing calibration techniques for obtaining th e external transformation of the lidar and the camera sensors have disadvantages in that special calibration objects are used or the object size is too large. In this paper, we introduce a simple calibration method between two sensors using a spherical object. We calculated the sphere center coordinates using four 3-D points selected by RANSAC of the range data of the sphere. The 2-dimensional coordinates of the object center in the camera image are also detected to calibrate the two sensors. Even when the range data is acquired from various angles, the image of the spherical object always maintains a circular shape. The proposed method results in about 2 pixel reprojection error, and the performance of the proposed technique is analyzed by comparing with the existing methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.9-14
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• 2019

A small millimeter-wave tracking radar is a pulse-based radar that searches, detects, and tracks a target in real time through a TWS (Track While Scan) method for a traps target on the sea with a large RCS running at low speed. It is necessary to develop a board equipped with a high-speed CPU to acquire and track target information through LPRF, DBS, and HRR signal processing techniques for a trap target operating various kinds of dexterous objects such as chaff and decoy, We designed a signal processor structure including DFT (Discrete Fourier Transform) module design that can perform real - time FFT operation using FPGA (Field Programmable Gate Array) and verified the signal processor implemented through performance test.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.100-103
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• 2019

Due to the fourth industrial revolution called 'fusion and connection', interest in 'high connectivity society' and 'highland society' is increasing, and related objects are not limited to automation and connected cars. The Internet of Things is the main concern of the 4th Industrial Revolution and it is expected to play an important role in establishing the basis of the next generation mobile communication service. Several domestic and foreign companies have been studying various types of LPWANs for the construction of the Internet based on things, and there is Semtech's LoRaWAN technology as representative. LoRaWAN is a long-distance, low-power network designed to manage a large number of devices and sensors, with communications from hundreds to thousands to thousands of devices and sensors. In this paper, we analyze the optimum node capacity of gateway for maximum performance while reducing resource waste in using LoRaWAN.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.445-452
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• 2019

Today, most detection systems used in the marine industry are the majority of devices operating in the high-power X-band bands. While most detection systems using these frequencies in the X-Band band can expect a wide range of detection performance, they are not suitable for precision detection and have the limitation that they are large and heavy. In this paper, we designed, fabricated and tested an electronic scanner capable of detecting not only the surrounding objects but also the ocean waves at a low power of less than 2W in the 17GHz frequency band of the Ku-Band. A high-performance patch array antenna and Doppler effect were utilized to obtain sufficient detection performance even at low power. As a result of the test, it was confirmed that the performance was sufficiently valuable.

• Physical Therapy Korea
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• v.26 no.2
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• pp.61-68
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• 2019

Background: Forward head posture (FHP) is a postural alignment of the cervical vertebrae that leads to increased gravitational load on cervical segmental motions. The overhead arm lift test assesses the ability to actively dissociate and control low cervical flexion and move the shoulders through overhead flexion. Objects: The purpose of this study was to explore muscle activities in the upper trapezius (UT), serratus anterior (SA), sternocleidomastoid (SCM), and lower trapezius (LT) alongside changes in head position during the overhead arm lift test in individuals with FHP. Methods: Fifteen subjects with forward head posture and fifteen subjects with normal subjcects were enrolled in this study. The patients performed the overhead arm lift test, and muscle activities of the UT, SCM, SA, and LT were measured using surface electromyography and by evaluating changes in head position. Independent t-tests were used to detect significant differences between the two groups and Cohen's d was calculated to measure the size of the mean difference between the groups. Results: The FHP group demonstrated significantly increased muscle activity of the UT ($32.46{\pm}7.64$), SCM ($12.79{\pm}4.01$), and LT ($45.65{\pm}10.52$) and significantly decreased activity in the SA ($26.65{\pm}6.15$) than the normal group. The change in head position was significantly higher in the FHP group ($6.66{\pm}2.08$) than the normal group. Effect sizes for all parameters assessed were large between the two groups. Conclusion: The subjects with excessive FHP displayed were unable to fix their heads in position during the overhead arm lift test. The overhead arm lift test can thus be used in clinical settings to confirm control of the neck in these subjects.

• Journal of the Korean Society of Mineral and Energy Resources Engineers
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• v.55 no.6
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• pp.614-623
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• 2018

The recent occurrence of consecutive large earthquakes in the southeastern part of the Korean peninsula has brought significant attention to the prevention of earthquake damage in Korea. This article aims to explore a technology-based approach for earthquake drills using state-of-the-art visual intelligence and virtual reality technologies. The technical process consists of several stages, including acquisition of image information in living spaces using a camera, recognition of objects from the acquired image information, extraction of three dimensional geometric information, simulation of virtual earthquakes using dynamic modelling techniques such as the discrete element method, and realization of the simulated earthquake in a virtual reality environment. This article provides a comprehensive analysis of the individual processes at each stage of the technical process, a survey on the current status of related technologies, and discussion of the technical challenges in its execution.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.311-318
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• 2021

Research has been ongoing to detect ships from offshore photographs for a variety of reasons, including maritime security, identifying international trends, and social scientific research. Due to the development of artificial intelligence, R-CNN models for object detection in photographs and images have emerged, and the performance of object detection has risen dramatically. Ship detection in offshore photographs using the R-CNN model has also begun to apply to satellite photography. However, satellite images project large areas, so various objects such as vehicles, landforms, and buildings are sometimes recognized as ships. In this paper, we propose a novel methodology to improve the performance of ship detection in satellite photographs using R-CNN series models. We separate land and sea via marker-based watershed algorithm and perform morphology operations to specify RoI one more time, then detect vessels using R-CNN family models on specific RoI to reduce typology. Using this method, we could reduce the misdetection rate by 80% compared to using only the Fast R-CNN.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.499-509
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• 2020

This study explores how to build object classification learning data based on artificial intelligence. The data has been investigated recently in image classification fields and, in turn, has a great potential to use. In order to recognize and extract relatively accurate objects using artificial intelligence, a large amount of learning data is required to be used in artificial intelligence algorithms. However, currently, there are not enough datasets for object recognition learning to share and utilize. In addition, generating data requires long hours of work, high expenses and labor. Therefore, in the present study, a small amount of initial aerial image learning data was used in the GAN (Generative Adversarial Network)-based generator network in order to establish image learning data. Moreover, the experiment also evaluated its quality in order to utilize additional learning datasets. The method of oversampling learning data using GAN can complement the amount of learning data, which have a crucial influence on deep learning data. As a result, this method is expected to be effective particularly with insufficient initial datasets.