• Journal of the Korea Institute of Information and Communication Engineering
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• 제24권5호
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• pp.576-583
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• 2020

This paper proposes a technique to determine the spam comments on YouTube, which have recently seen tremendous growth. On YouTube, the spammers appeared to promote their channels or videos in popular videos or leave comments unrelated to the video, as it is possible to monetize through advertising. YouTube is running and operating its own spam blocking system, but still has failed to block them properly and efficiently. Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naive Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel) and ensemble model combining these techniques in the comment data from popular music videos - Psy, Katy Perry, LMFAO, Eminem and Shakira.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제26권10호
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• pp.1469-1476
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• 2022

Recently, many researches have been conducted to estimate blood pressure using ECG(Electrocardiogram) and PPG(Photoplentysmography) signals. In this paper, we designed and implemented a mobile system to monitor blood pressure in real time by using 1-D convolutional neural networks. The proposed model consists of deep 11 layers which can learn to extract various features of ECG and PPG signals. The simulation results show that the more the number of convolutional kernels the learned neural network has, the more detailed characteristics of ECG and PPG signals resulted in better performance with reduced mean square error compared to linear regression model. With receiving measurement signals from wearable ECG and PPG sensor devices attached to the body, the developed system receives measurement data transmitted through Bluetooth communication from the devices, estimates systolic and diastolic blood pressure values using a learned model and displays its graph in real time.

• Journal of Korean Society of Steel Construction
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• 제21권2호
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• pp.165-173
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• 2009

Hanger cables in suspension bridges are constrained by the horizontal clamp. So, the accuracy of estimated tension of hange cable using existing methods based on the simple mathematical model of singel cable decreases as the length of cable decreases because of the flexural rigidity. Therefore, back analysis and system identification techniques based on the finite element model are proposed recently. In this paper, the applicability of the back analysis and system identification techniques are compared using the hanger cable of Gang-An Bridge. The experimental results show that the back analysis and system identification techniques are more reliable than the existing string theory and linear regression method in the view point of the error of natural frequencies. However, the estimation error of tension can be varied according to the accuracy of finite element model in the model based methods. Especially, the boundary condition is more affective when the length of cable is short, so it is important to identify the boundary condition through experiment if it is possible. The tension estimation method using system identification technique is more attractive because it can easily consider the boundary condition and it is not sensitive to the number of input measured natural frequencies.

• Journal of the Korea Concrete Institute
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• 제21권5호
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• pp.661-668
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• 2009

Effective beam width model(EBWM) has been used for analysis of post-tensioned(PT) flat plate slab frames under lateral loads. The accuracy of this model in predicting lateral drifts and unbalanced moments strongly depends on the estimated effective stiffness of PT flat plate slabs. As moments on the slab due to lateral loads increases, cracks occur which leads to stiffness reduction in slabs. For analyzing PT flat plate slab structure under lateral loads with good precision, reduction in slab stiffness has to be accurately estimated for EBWM. For this purpose, this study collected test results of PT flat plate system conducted by former researches. And this study reduced the width of slab so that the stiffness of the EBWM converged into the lateral stiffness of each test specimens by trial and error. By conducting nonlinear regression analysis using the stiffness ratio of the reduced width of slab to the effective width of EBWM with respect to the level of slab moments, an equation for calculating stiffness reduction factor for slab is proposed. For verifying the accuracy of the proposed equation, this study compared with the test result of the PT flat plate frame. It is shown that the EBWM with the proposed equation predicts the actual stiffness of the PT specimen which varied according to the level of applied moment.

• Journal of the Institute of Electronics Engineers of Korea SP
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• 제43권3호
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• pp.25-33
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• 2006

This paper proposed device-independent color matching algorithm based on the 3D RGB lookup table (LUT) between mobile camera and mobile LCD (Liquid Crystal Display) to improve the color-fidelity. Proposed algorithm is composed of thee steps, which is device characterization, gamut mapping, 3D RGB-LUT design. First, the characterization of mobile LCD is executed using the sigmoidal function, different from conventional method such as GOG (Gain Offset Gamma) and S-curve modeling, based on the observation of electro-optical transfer function of mobile LCD. Next, mobile camera characterization is conducted by fitting the digital value of GretagColor chart captured under the daylight environment (D65) and tristimulus values (CIELAB) using the polynomial regression. However, the CIELAB values estimated by polynomial regression exceed the maximum boundary of the CIELAB color space. Therefore, these values are corrected by linear compression of the lightness and chroma. Finally, gamut mapping is used to overcome the gamut difference between mobile camera and moible LCD. To implement the real-time processing, 3D RGB-LUT is designed based on the 3D RGB-LUT and its performance is evaluated and compared with conventional method.

• Journal of Intelligence and Information Systems
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• 제28권4호
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• Clinical and Experimental Pediatrics
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• 제48권1호
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• pp.75-80
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• 2005

Purpose : Internal tibial torsion is prevalent in East Asian countries such as Korea and Japan, where sitting on the floor is common behavior. Internal tibial torsion or excessive lateral tibial torsion may cause esthetical, functional, or psychological problems and also may induce degenerative arthritis in older age. The purpose of this study is to measure the tibial torsion in children of the Jeju area. Methods : Tibial torsion was measured in 1,042 lower extremities of 521 children from one to 12 years of age. The values of transmalleolar angles were analyzed for each age group divided by 6 months. Quadratic and linear regression models were used to fit patterns of changes in mean values of transmalleolar angles. The age at seven, which provides the highest coefficient of determination for quadratic regression analysis, was used as a cut-off point to fit different statistical models. Results : The mean transmalleolar angle was $0.10{\pm}5.79^{\circ}$ in all children,$ 0.90{\pm}5.49^{\circ}$ in males, and $-0.80{\pm}5.97^{\circ}$ in females. The value was $4.25{\pm}4.04$ in 1 year of age, gradually decreased to the lowest level of $-1.98^{\circ}$ in four years and seven months of age, increased again with age until it reached $0.67{\pm}1.10^{\circ}$ at seven years of age, and stayed at that level thereafter. Conclusion : Internal tibial torsion in infancy is known to correct spontaneously in the normal developing process. But in this study, the mean transmalleolar angle in children of Jeju area annually decreased after one year of age; to the lowest angle at four years and seven months of age; increased again gradually to the age of seven; and persisted in that level, about $10^{\circ}$ less than western children, not correcting further thereafter. These findings suggest tibial torsion might be caused by lifestyle, especially sitting on feet. To prevent abnormalities of joints and gaits, early diagnosis of tibial torsion in childhood and posture correction or early treatment when needed, seems to be necessary.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2011년도 정기 학술발표대회
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• pp.38-38
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• 2011

일반적으로 레일마모는 열차의 주행안전 및 승차감에 미치는 영향이 크고, 소음 진동의 주요원인으로 작용한다. 또한 레일마모가 발생할 경우 궤도구조의 파괴를 촉진시킴으로써 차량 및 궤도유지보수비를 크게 증가시킨다. 따라서 구간 특성 및 환경 영향 인자 등 현장에서 발생하는 마모 원인을 체계적으로 분석함으로써 마모를 저감할 수 있도록 차량운행 조건과 선로선형 및 궤도구조를 설계하는 것은 중요한 과제이다. CART(Classification And Regression Tree; 분류와 회귀나무) 분석은 패키지화된 좋은 분류 및 예측도구 기법으로 나무의 상위 분리수준에서 일반적으로 나타나는 가장 중요한 입력변수들을 사용하는 등의 입력변수를 선정하는 경우 매우 유용하다. 본 연구에서는 다변수 구간특성 및 환경인자를 고려한 검측 자료 상관관계 분석을 위한 회귀 나무기반 모델(TBM: Tree Based Model) 분석 수행을 위해 지하철 2호선 마모 데이터와 마모 데이터에 영향을 미치는 각종 다변수 구간특성 및 환경인자를 사용하였다. 2호선 지하철의 구간특성 인자 및 환경인자는 레일의 종류, 레일의 위치, 도상, 곡률반경, 캔트 슬랙 및 운행 일수 등으로 구분하였다. 레일의 종류는 ks-50kg과 ks-60kg 두 종류의 레일이 있으며, 레일의 위치는 지상과 지하로 크게 구분할 수 있다. 도상은 콘크리트 도상, 자갈 도상과 일부 구간의 방진상 콘크리트 도상으로 구분할 수 있으며, 곡률반경은 직선구간과 완화곡선 구간 및 최소 250m부터 627m까지 분포된 원 곡선 구간으로 구분할 수 있다. 캔트 간격은 최소 96cm 부터 120cm 간격으로 구분하며, 슬랙은 5~9cm에 분포하고, 운행 기간은 해당 기간 동안 유지보수 이력이 없는 구간을 선정하여 2005년부터 2006년까지 4번에 걸쳐 검측된 지하철 2호선 내선 마모데이터를 사용하였다. 총 X1부터 X7까지 총 7개의 구간특성 또는 환경특성을 영향인자로 선정하였으며, 이러한 영향인자에 의해 결정되는 종속 인자로 Y1인 직마모와 Y2인 측마모를 선정하여 이 중 실질적으로 지하철 궤도의 성능 평가에 주요 판단인자로 사용되는 측마모와 구간특성 및 환경영향인자와의 상관관계 분석을 수행하였다. 해당 마모 데이터가 검측되는 기간 동안 유지보수 이력이 없는 12272 point의 데이터를 검출하였고 CART 프로그램을 이용하여 데이터를 분석하였으며, CART 프로그램의 해석을 위해 종속변수인 직마모량은 각 검측 지점의 마모량에 해당하는 등급으로 변환하여 분석을 수행하였다. 레일의 마모에 영향을 미치는 구간특성 및 환경인자와 종속 변수로 사용된 레일의 마모량 사이의 CART를 이용한 상관관계 분석은 실제 구조물에서 영향인자간의 상관 관계와 유사하며, 추후 연구에서는 이를 바탕으로 하여 정량화된 검측 데이터를 종속변수로 하여 구간특성 또는 환경인자 등 외부 영향인자를 고려한 궤도 검측데이터와의 상관관계 분석을 수행할 계획이다.

• Korean journal of applied entomology
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• 제52권1호
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• pp.5-12
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• 2013

The Hawaiian beet webworm (Spoladea recurvalis) is one of the serious insect pests found on red beet (Beta vulgaris var. conditiva) in Korea. The study was conducted to investigate the development period of S. recurvalis at various constant temperatures, 15.0, 17.5, 20.0, 22.5, 25.0, 27.5, 30.0, 32.5 and $35.0^{\circ}C$, with $65{\pm}5%$ RH and a photoperiod of 16L:8D. The developmental period from egg to pre-adult was 51.0 days at $17.5^{\circ}C$ and 14.6 days at $35.0^{\circ}C$. The developmental period of S. recurvalis was decreased with increasing temperature. The relationship between the developmental rate and temperature was fitted well by linear regression analysis ($R^2{\geq}0.87$). The lower developmental threshold and effective accumulative temperature of the total immature stage were $10.4^{\circ}C$ and 384.7 degree days, respectively. The nonlinear relationship between the temperature and developmental rate was well described by the Lactin model. The relationship between the cumulative frequency and normalized distributions of the developmental period for each life stage were fitted to the Weibull function with $R^2=0.63{\sim}0.87$.

• Tunnel and Underground Space
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• 제30권6호
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• pp.540-550
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• 2020

Machine learning has been actively used in the field of automation due to the development and establishment of AI technology. The important thing in utilizing machine learning is that appropriate algorithms exist depending on data characteristics, and it is needed to analysis the datasets for applying machine learning techniques. In this study, advance rate is predicted using geotechnical and machine data of TBM tunnel section passing through the soil ground below the stream. Although there were no problems of application of statistical technology in the linear regression model, the coefficient of determination was 0.76. While, the ensemble model and support vector machine showed the predicted performance of 0.88 or higher. it is indicating that the model suitable for predicting advance rate of the EPB Shield TBM was the support vector machine in the analyzed dataset. As a result, it is judged that the suitability of the prediction model using data including mechanical data and ground information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of data.