• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2321-2326
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• 2011

Cervical vertebrae are a complex structure and an important part of human body connecting the head and the trunk. In this paper, we propose a method to extract sternocleidomastoid muscle from ultrasonography images of cervical vertabrae automatically. In our method, Region of Interests(ROI) is extracted first from an ultrasonography image after removing unnecessary auxiliary information such as metrics. Then we apply Ends-in search stretching algorithm in order to enhance the contrast of brightness. Average binarization is then applied to those pixels which its brightness is sufficiently large. The noise part is removed by image processing algorithms. After extracting fascia encloses sternocleidomastoid muscle, target muscle object is extracted using the location information of fascia according to the number of objects in the fascia. When only one object is to be extracted, we search downward first to extract the target muscle area and then search from right to left to extract the area and merge them. If there are two target objects, we extract first from the upper-bound of higher object to the lower-bound of lower object and then remove the fascia of the target object area. Smearing technique is used to restore possible loss of the fat area in the process. The thickness of sternocleidomastoid muscle is then calculated as the maximum thickness of those extracted objects. In this experiment with 30 real world ultrasonography images, the proposed method verified its efficacy and accuracy by health professionals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.149-154
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• 2014

Quality is the value that can be measured by observing the characteristics of the service quantity or quality. QoS is predictable service traffic to a minimum requirements what passed in network. In the course of Smart Medical Information System Development there exist some functional requirements to satisfy quality objectives. The functional smart domains of healthcare information systems consists of Patient Module, a smart sensing and communication domain, RFID Tag Readers and the behavior domain, Homecare Station Domain, Clinical Station. This study is performed on evaluation methodology of u-health service satisfaction quality of each domain. In this paper QoS metrics and the quality of medical information requirements, functional requirements are separated by. Quality parameters consists of six items and the functional requirements and quality requirements 20 details the five items and consist of 20 detailed items. On this study the quality evaluation methodology of Korean smart health information quality assessment matrix 2 - factor evaluation method is proposed. The overall framework of this paper is organizing the specific criteria of quality of medical information system and modeling quality evaluation process under all smart environment.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.11
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• pp.1055-1063
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• 2013

Low noise amplifiers(LNAs) are an integral component of RF receivers and are frequently required to operate at wide frequency bands for various wireless systems. For wideband operation, important performance metrics such as voltage gain, return loss, noise figures and linearity have been carefully investigated and characterized for the proposed LNA. An inductive shunt feedback configuration is successfully employed in the input stage of the proposed LNA which incorporates cascaded networks with a peaking inductor in the buffer stage. Design equations for obtaining low and high input matching frequencies are easily derived, leading to a relatively simple method for circuit implementation. Careful theoretical analysis explains that poles and zeros are characterized and utilized for realizing the wideband response. Linearity is significantly improved because the inductor between gate and drain decreases the third-order harmonics at the output. Fabricated in $0.18{\mu}m$ CMOS process, the chip area of this LNA is $0.202mm^2$, including pads. Measurement results illustrate that input return loss shows less than -7 dB, voltage gain greater than 8 dB, and a little high noise figure around 7~8 dB over 1.5~13 GHz. In addition, good linearity(IIP3) of 2.5 dBm is achieved at 8 GHz and 14 mA of current is consumed from a 1.8 V supply.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.981-985
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• 2004

In this paper we designed the motor control IP Core and evaluate its quality from the viewpoint of IP reuse. The most attractive merit of this methodology, so called IP-based hardware design, is hardware reuse. Although various vendors designed hardware with the same specification and got the same functional results, all that IPs is not the same quality in the reuse aspect. As tremendous calls for SoC have been increased, associated research about IP quality standard, VSIA(Virtual Socket Interface Alliance) and STARC(Semiconductor Technology Academic Research Center), has been doing best to make the IP quality evaluation system. And they made what conforms to objective IP design standard. We suggest the methodology to evaluate our own designed motor control IP quality with this standard. To attain our goal, we designed motor control IP that could control the motor velocity and position with feedback compensation algorithm. This controller has some IP blocks : digital filter, quadrature decoder, position counter, motion compensator, and PWM generator. Each block's functionality was verified by simulator ModelSim and then its quality was evaluated. To evaluate the core, We use Vnavigator for lint test and ModelSim for coverage check. During lint process, We adapted the OpenMORE's rule based on RMM (Reuse Methodology Manual) and it could tell us our IP's quality in a manner of the scored value form. If it is high, its quality is also high, and vice versa. During coverage check ModelSim-SE is used for verifying how our test circuits cover designs. This objective methods using well-defined commercial coverage metrics could perform a quantitative analysis of simulation completeness. In this manner, We evaluated the designed motor control IP's quality from the viewpoint of reuse. This methodology will save the time and cost in designing SoC that should integrate various IPs. In addition to this, It can be the guide for comparing the equally specified IP's quality. After all, we are continuously looking forward to enhancing our motor control IP in the aspect of not only functional perfection but also IP reuse to prepare for the SoC-Compliant motor control IP design.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.265-272
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• 2020

In the research of brain computer interface (BCI) technology, one of the big problems encountered is how to deal with some people as called the BCI-illiteracy group who could not control the BCI system. To approach this problem efficiently, we investigated a kind of spectral EEG characteristics in the prior resting state in association with BCI performance in the following BCI tasks. First, spectral powers of EEG signals in the resting state with both eyes-open and eyes-closed conditions were respectively extracted. Second, a convolution neural network (CNN) based binary classifier discriminated the binary motor imagery intention in the BCI task. Both the linear correlation and binary prediction methods confirmed that the spectral EEG characteristics in the prior resting state were highly related to the BCI performance in the following BCI task. Linear regression analysis demonstrated that the relative ratio of the 13 Hz below and above the spectral power in the resting state with only eyes-open, not eyes-closed condition, were significantly correlated with the quantified metrics of the BCI performance (r=0.544). A binary classifier based on the linear regression with L1 regularization method was able to discriminate the high-performance group and low-performance group in the following BCI task by using the spectral-based EEG features in the precedent resting state (AUC=0.817). These results strongly support that the spectral EEG characteristics in the frontal regions during the resting state with eyes-open condition should be used as a good predictor of the following BCI task performance.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.102-113
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• 2005

Ecosystem is composed of human, biotic and abiotic environment. Landscape is an ecosystem which appear in a unit region. These landscape are the spatiotemporal land mosaic which is combined with various landscape elements. And, land use and land cover changes are important factors of landscape structure changes. This study is mainly focused on the analysing the spatiotemporal change patterns of Daegu metropolitan sphere forest landscape, using landscape indices and Ecosystem Service Value (ESV) which quantify ecosystem structures and functions. The results of this study are as follow: The encroachment and fragmentation of forest were due to linear developments, i. e. road construction, rather than large-scale developments such as residental lands or industrial complexes. And, the core area percentages of landscape gradually decreased and these could possibly deteriorate the soundness of forest areas by reducing the core areas which are habitats of species. In addition, there was intimate relations between ESV and forest landscape area. The results of this study can be detached standards for impartial judgements between the logic of development & conservation, and basic standards for the establishment of development plans, i. e. metropolitan-plans, which are adequately reflected ecosystem value.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.97-107
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• 2020

This study aims to conduct a comprehensive meta-study from the perspective of content analysis to explore trends in Korean academic research on the sharing economy by using the big data analytics. Comprehensive meta-analysis methodology can examine the entire set of research results historically and wholly to illuminate the tendency or properties of the overall research trend. Academic research related to the sharing economy first appeared in the year in which Professor Lawrence Lessig introduced the concept of the sharing economy to the world in 2008, but research began in earnest in 2013. In particular, between 2006 and 2008, research improved dramatically. In order to grasp the overall flow of domestic academic research of trends, 8 years of papers from 2013 to the present have been selected as target analysis papers, focusing on titles, keywords, and abstracts using database of electronic journals. Big data analysis was performed in the order of cleaning, analysis, and visualization of the collected data to derive research trends and insights by year and type of literature. We used Python3.7 and Textom analysis tools for data preprocessing, text mining, and metrics frequency analysis for key word extraction, and N-gram chart, centrality and social network analysis and CONCOR clustering visualization based on UCINET6/NetDraw, Textom program, the keywords clustered into 8 groups were used to derive the typologies of each research trend. The outcomes of this study will provide useful theoretical insights and guideline to future studies.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.181-194
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• 2017

This study aims to develop a regression model for forest volume estimation using field-collected forest inventory information and airborne LiDAR data. The response variable of the model is forest stem volume, was measured by random sampling from each individual plot of the 30 circular sample plots collected in Bonghwa-gun, Gyeong sangbuk-do, while the predictor variables for the model are Height Percentiles(HP) and Height Bin(HB), which are metrics extracted from raw LiDAR data. In order to find the most appropriate model, the candidate models are constructed from simple linear regression, quadratic polynomial regression and multiple regression analysis and the cross-validation tests were conducted for verification purposes. As a result, $R^2$ of the multiple regression models of $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}$ among the estimated models was the highest at 0.509, and the PRESS statistic of the simple linear regression model of $HP_{25}$ was the lowest at 122.352. $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}-based$ models, thus, are comparatively considered more appropriate for Korean forests with complicated vertical structures.

• Journal of KIISE
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• v.44 no.2
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• pp.213-222
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• 2017

The Internet of Things (IoT) technology is rapidly developing in recent years, and is applicable to various fields. A smart factory is one wherein all the components are organically connected to each other via a WSN, using an intelligent operating system and the IoT. A smart factory technology is used for flexible process automation and custom manufacturing, and hence needs adaptive network management for frequent network fluctuations. Moreover, ensuring the timeliness of the data collected through sensor nodes is crucial. In order to ensure network timeliness, the power consumption for information exchange increases. In this paper, we propose an IEEE 802.15.4e DSME-based distributed scheduling algorithm for mobility support, and we evaluate various performance metrics. The proposed algorithm adaptively assigns communication slots by analyzing the network traffic of each node, and improves the network reliability and timeliness. The experimental results indicate that the throughput of the DSME MAC protocol is better than the IEEE 802.15.4e TSCH and the legacy slotted CSMA/CA in large networks with more than 30 nodes. Also, the proposed algorithm improves the throughput by 15%, higher than other MACs including the original DSME. Experimentally, we confirm that the algorithm reduces power consumption by improving the availability of communication slots. The proposed algorithm improves the power consumption by 40%, higher than other MACs.

• Journal of The Korean Association of Information Education
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• v.20 no.3
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• pp.293-302
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• 2016

It is the latest fashion of interesting with software education in public school environment and also consider as high priority issue of curriculum for college freshman with programming 101 courses. The block-based programming tool is used widely for the beginner and provides several positive features compare than text-based programming language tools. To measure quality of programming code elaborately which is based script language, it is need to very tough manual process. As a result the previously research related with evaluation of block-based script code has been focused very simple methods in which normalize the number of blocks used which is related with programming concept. In such cases in this, it is difficult to measure structural vulnerability of script code and implicit programming concept which does not expose. In this research, the framework is proposed which enable to measure and evaluate quality of code script of block-based programming tools and also provides method to find of vulnerability of script code. In this framework, the quality metrics is constructed to structuralize implicit programming concept and then developed the quality measure and vulnerability model of script to improve level of programming. Consequently, the proposed methods enable to check of level of programming and predict the heuristic target level.