• Journal of KIISE
• /
• v.42 no.1
• /
• pp.130-137
• /
• 2015

With the rapid increase of energy consumption and environmental problems, the need for green techniques is increasing. Network coding can provide a solution by reducing unnecessary data transmission and by estimating traffic patterns. In addition, it can amplify the synergy with the cognitive radio network (CR) since the CR has recognition and optimal decision functionalities. In this paper, we propose a network coding based green cognitive radio network. With the simulations, we show that the proposed scheme is up to 25% better than the previous work.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.8 no.2
• /
• pp.63-72
• /
• 2004

The purpose of this study is to evaluate seismic performance of reinforced concrete bridge piers supported by laminated rubber bearings. A computer program, named RCAHEST(Reinforced Concrete Analysis in Higher Evaluation System Technology), for the analysis of reinforced concrete structures was used. Material nonlinearity is taken into account by comprising tensile, compressive and shear models of cracked concrete and a model of reinforcing steel. The smeared crack approach is incorporated. Seismic isolator element is developed to predict behaviors of laminated rubber bearings. The proposed numerical method for seismic performance evaluation of reinforced concrete bridge piers supported by laminated rubber bearings is verified by comparison with reliable experimental results.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2018.11a
• /
• pp.77-80
• /
• 2018

360 VR 콘텐츠는 기존의 선형적인 일반 영상에 비해 사용자에게 더 많은 정보와 높은 몰입감을 제공할 수 있어 의학, 군사, 교육, 게임 등 다양한 분야에서 활용되고 있다. 최근에는 모바일 기기의 성능 향상과 통신기술의 발달에 힘입어 모바일 네트워크를 사용한 360 VR 콘텐츠 소비가 증가하는 추세이다. 모바일 네트워크는 대역폭이 한정적이고 가변적인 특성이 있어 이를 통해 용량이 큰 360 VR 콘텐츠 전송 시 초기 접속 지연 및 재생 끊김이 발생하여 사용자의 만족도를 감소시킬 수 있다. 이에 본 논문은 위에 언급한 문제를 해결하기 위해 360 VR 콘텐츠 전송 시 전체 요구대역폭을 감소시키고 사용자 초기 접속 속도를 향상시키는 것을 목표로, 360 VR 콘텐츠의 지오메트리 값과 사용자의 요(i.e. yaw)값을 활용하여 각도 기반으로 사용자의 현재 시점에 해당하는 타일을 확인하고 해당 타일에 높은 비트율을 할당하는 방법 및 웹 기반 전송에 대해 연구 개발하였다. 이를 위하여 웹 기반 3D 렌더링 API 인 WebVR API, HTTP Adaptive Streaming 기술의 표준 MPEG-DASH의 dash.js API를 활용하여 개발하고, 성능 확인 실험을 통해 요구대역폭 감소, 클라이언트 접속 속도 향상을 제시한다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.100-100
• /
• 2020

감조하천은 일반하천과 달리 다양한 수문 요소에 영향을 받아 비선형적인 수문 특성을 보이고 있기 때문에 수위-유량 관계곡선식이 개발되어 있지 않다. 본 연구에서는 조위의 영향을 받는 감조하천에서 강우의 영향으로 인한 수위-유량 관계곡선식의 작성 방법론을 제안하고자 하였다. 이를 위해 울산 수위시계열을 wavelet 분석, curve fitting, high pass filter 방법을 이용하여 4가지의 성분(조석 성분, 파고 성분, 강우-유출 성분, 잡음 성분)으로 분리하고, 분포형 모형인 GRM 모형을 통해 유출량을 산정하였다. 모의 유출량과 강우-유출 성분을 이용하여 수위-유량 관계곡선식을 개발하고, 모의 유출량에 따른 수위를 추정하였다. 나머지 3가지 성분과 합하여 통합 수위를 산정하고 관측 유량과 비교한 결과 오차가 약 10% 이내로 본 방법론이 적용성이 있음을 확인하였다. 본 연구결과를 활용한다면 홍수기에 감조하천에서 수위를 정확히 예측하는데 기여할 수 있을 것으로 판단된다.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.4
• /
• pp.473-481
• /
• 2024

In this paper, we introduce a newly developed winding model to simulate the motion of underwater cable consisting of winch drums. It is assumed that only tension affects the underwater cable motion. This assumption is suitable for simulating the underwater cable motion towed by a navel vessel in a straight ahead maneuver. The underwater cable is discretized using Nodal Position Finite Element Method. This numerical method is known to be suitable for predicting the underwater cable motion with large deformation because it can express geometric nonlinearity. In this paper, the validity of the numerical method was secured by comparing it with the depth information of towing cable measured through sea experiments.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.35 no.1
• /
• pp.173-185
• /
• 2015

This study aims to investigate the characteristics and types of car accidents and establish a prediction model by analyzing 456 car accidents having occurred in the 11 tunnels in Busan, through statistical analysis techniques. The results of this study can be summarized as below. As a result of analyzing the characteristics of car accidents, it was found that 64.9% of all the car accidents took place in the tunnels between 08:00 and 18:00, which was higher than 45.8 to 46.1% of the car accidents in common roads. As a result of analyzing the types of car accidents, the car-to-car accident type was the majority, and the sole-car accident type in the tunnels was relatively high, compared to that in common roads. Besides, people at the age between 21 and 40 were most involved in car accidents, and in the vehicle type of the first party to car accidents, trucks showed a high proportion, and in the cloud cover, rainy days or cloudy days showed a high proportion unlike clear days. As a result of analyzing the principal components of car accident influence factors, it was found that the first principal components were road, tunnel structure and traffic flow-related factors, the second principal components lighting facility and road structure-related factors, the third principal factors stand-by and lighting facility-related factors, the fourth principal components human and time series-related factors, the fifth principal components human-related factors, the sixth principal components vehicle and traffic flow-related factors, and the seventh principal components meteorological factors. As a result of classifying car accident spots, there were 5 optimized groups classified, and as a result of analyzing each group based on Quantification Theory Type I, it was found that the first group showed low explanation power for the prediction model, while the fourth group showed a middle explanation power and the second, third and fifth groups showed high explanation power for the prediction model. Out of all the items(principal components) over 0.2(a weak correlation) in the partial correlation coefficient absolute value of the prediction model, this study analyzed variables including road environment variables. As a result, main examination items were summarized as proper traffic flow processing, cross-section composition(the width of a road), tunnel structure(the length of a tunnel), the lineal of a road, ventilation facilities and lighting facilities.

• Korean journal of applied entomology
• /
• v.51 no.4
• /
• pp.431-438
• /
• 2012

The developmental time of the nymphs of Myzus persicae was studied in the laboratory (six constant temperatures from 15 to $30^{\circ}C$ with 50~60% RH, and a photoperiod of 14L:10D) and in a green-pepper plastic house. Mortality of M. persicae in laboratory was high in the first(6.7~13.3%) and second instar nymphs(6.7%) at low temperatures and high in the third (17.8%) and fourth instar nymphs(17.8%) at high temperatures. Mortality was 66.7% at $33^{\circ}C$ in laboratory and $26.7^{\circ}C$ in plastic house. The total developmental time was the longest at $14.6^{\circ}C$ (14.4 days) and shortest at $26.7^{\circ}C$ (6.0 days) in plastic house. The lower threshold temperature of the total nymphal stage was $3.0^{\circ}C$ in laboratory. The thermal constant required for nymphal stage was 111.1DD. The relationship between developmental rate and temperature was fitted nonlinear model by Logan-6 which has the lowest value on Akaike information criterion (AIC) and Bayesian information criterion (BIC). The distribution of completion of each developmental stage was well described by the 3-parameter Weibull function ($r^2=0.95{\sim}0.97$). This model accurately described the predicted and observed occurrences. Thus the model is considered to be good for use in predicting the optimal spray time for Myzus persicae.

• Korean journal of applied entomology
• /
• v.51 no.4
• /
• pp.421-429
• /
• 2012

The developmental time period of Aphis gossypii was studied in laboratory (six constant temperatures from 15 to $30^{\circ}C$ with 50~60% RH, and a photoperiod of 14L:10D) and in a cucumber plastic house. The mortality of A. gossypii in the laboratory was high in the 2nd (20.0%) and 3rd stage(13.3%) at low temperature but high in the 3rd (26.7%) and 4th stage (33.3%) at high temperatures. Mortality in the plastic house was high in the 1st and 2nd stage but there was no mortality in the 4th stage at low temperature. The total developmental period was longest at $15^{\circ}C$ (12.2 days) in the laboratory and shortest at $28.5^{\circ}C$ (4.09 days) in the plastic house. The lower threshold temperature at the total nymphal stage was $6.8^{\circ}C$ in laboratory. The thermal constant required to reach the total nymphal stage was 111.1DD. The relationship between the developmental rate and temperature fit the nonlinear model of Logan-6 which has the lowest value for the Akaike information criterion(AIC) and Bayesian information criterion(BIC). The distribution of completion of each development stage was well described by the 3-parameter Weibull function ($r^2=0.89{\sim}0.96$). This model accurately described the predicted and observed outcomes. Thus it is considered that the model can be used for predicting the optimal spray time for Aphis gossypii.

• Elastomers and Composites
• /
• v.45 no.4
• /
• pp.263-271
• /
• 2010

Intermittent CSR testing was used to investigate the degradation of an FKM O-ring, also the prediction of its life-time. An intermittent CSR jig was designed taking into consideration the O-ring's environment under use. The testing allowed observation of the effects of friction, heat loss, and stress relaxation by the Mullins effect. Degradation of O-rings by thermal aging was observed between 60 and $160^{\circ}C$. In the high temperature of range ($100-160^{\circ}C$) O-rings showed linear degradation behavior and satisfied the Arrhenius relationship. The activation energy was about 60.2 kJ/mol. From Arrhenius plots, predicted life-times were 43.3 years and 69.9 years for 50% and 40% failure conditions, respectively. Based on TTS (time-temperature superposition) principle, degradation was observed at $60^{\circ}C$, and could save testing time. Between 60 and $100^{\circ}C$ the activation energy decreased to 48.3 kJ/mol. WLF(William-Landel-Ferry) plot confirmed that O-rings show non-linear degradation behavior under $80^{\circ}C$. The life-time of O-rings predicted by TTS principle was 19.1 years and 25.2 years for each failure condition. The life-time predicted by TTS principle is more conservative than that from the Arrhenius relationship.

• Journal of Wetlands Research
• /
• v.25 no.4
• /
• pp.306-314
• /
• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.