• 한국물환경학회지
• /
• 제37권4호
• /
• pp.286-295
• /
• 2021

The removal efficiency of road-deposited sediment (SDR) by road sweeping was analyzed by performing particle size analysis before and after road sweeping at four highways during May to December 2019. The SDR accounted for the largest proportion in the range of 250 to 850 ㎛ and the degree of its proportion had an effect on the particle size distribution curve. The particle size distribution of the collected sediments showed a similar distribution at all sites. Below 75 ㎛, the removal efficiency of SDR showed a constant value around 40%, but above 75 ㎛, it increased as the particle size increased. The removal efficiency was 82-90% (average 86%) for gravel, 66-93% (average 79%) for coarse sand, 35-92% (average 64%) for fine sand, 29-69% (average 44%) for very fine sand, 19-58% (average 40%) for silt loading, 10-59% (average 40%) for TSP, 13-57% (average 40%) for PM10, and 15-61% (average 38%) for PM2.5. SDR removal efficiency showed an average of 69% for the four highways. It was found that if the amount of SDR was less than 100 g/m², it was affected by the road surface condition and had a large regional deviation. As such, the amount of SDR and the removal efficiency increased. The fine particles, which have relatively low removal efficiency, contained a large amount of pollutants, which is an important factor in water and air pollution. Therefore, various measures to improve the removal efficiency of fine particles in SDR by road sweeping are needed.

• 산업경영시스템학회지
• /
• 제40권4호
• /
• pp.147-153
• /
• 2017

The mortality rate in industrial accidents in South Korea was 11 per 100,000 workers in 2015. It's five times higher than the OECD average. Economic losses due to industrial accidents continue to grow, reaching 19 trillion won much more than natural disaster losses equivalent to 1.1 trillion won. It requires fundamental changes according to industrial safety management. In this study, We classified the risk of accidents in industrial complex of Ulju-gun using spatial analytics and data mining. We collected 119 data on accident data, factory characteristics data, company information such as sales amount, capital stock, building information, weather information, official land price, etc. Through the pre-processing and data convergence process, the analysis dataset was constructed. Then we conducted geographically weighted regression with spatial factors affecting fire incidents and calculated the risk of fire accidents with analytical model for combining Boosting and CART (Classification and Regression Tree). We drew the main factors that affect the fire accident. The drawn main factors are deterioration of buildings, capital stock, employee number, officially assessed land price and height of building. Finally the predicted accident rates were divided into four class (risk category-alert, hazard, caution, and attention) with Jenks Natural Breaks Classification. It is divided by seeking to minimize each class's average deviation from the class mean, while maximizing each class's deviation from the means of the other groups. As the analysis results were also visualized on maps, the danger zone can be intuitively checked. It is judged to be available in different policy decisions for different types, such as those used by different types of risk ratings.

• 한국항해학회지
• /
• 제21권1호
• /
• pp.1-11
• /
• 1997

Because of the sharp increase of its export and import container cargo volumes contrast to the lack of related Container Terminal facility, equipment and inefficient procedure, there is now heavy container cargo congestions in Pusan Container Terminal. As a result of such a situation, many container ships avoid their calls into Pusan port. This is a major cause that in tum kads to weakening intemational competitiveness of the Korean industry. This study, therefore, aims are to make a quantitative analysis of Container Terminal System through the computer simulation, especially focusing on its 4 sub-system of a handling system, 'it is checked whether the current operation is being performed effectively through the computer simulation. The overall findings are as folIows; Firstly, average tonnage of the ships visiting the BCTOC was 32,360 G/T in from January '96, to may '96. The average arrival interval and service time of container ships at BCTOC are 5.63 hours and 18.67 hours respectively. Ship's arrival and service pattern at BCTOC was exponential distribution with 95% confidence and Erlang-4 distribution with 99% confidence. Secondly, average waiting time and number of ships was 9.9 hours, 235 ships(38%) among 620 ships. Number of stevedoring container per ship was average 747.7 TED, standard deviation 379.1 TEU and normal distribution with 99% confidence. Thirdly, from the fact that the average storage days of containers at BCTOC are 2.75 days (3.0 days when import, 2.5 days when export). it is founds that most containers were transfered to the off-dock storage areas with the free periods(5 days when import, 4 days when export), the reason for which is considered to be the insufficient storage area at BCTOC. Fourthly, in the case of gate in-out at BCTOC, occupied containers and emptied containers are 89% and 11% respectively in the gate-in, 75% and 25% seperately in the gate-out. Finally, from the quantitative analysis results for container terminal at BCTOC, ship's average wating time of ships was found to be 20.77 hours and berth occupancy rate(${\rho}$) was 0.83. 5~6 berths were required in order that the berth occupancy rate(${\rho}$) may be maintained up to 60% degree.

• 대한건축학회논문집:구조계
• /
• 제34권2호
• /
• pp.83-90
• /
• 2018

This study was conducted to investigate the living environment of semi-basement housing units in multi-household houses. It analyzed measurements of temperature and humidity obtained from the housing units. For this study, temperature and humidity sensors were installed in 10 semi-basement housing units to measure interior temperature and humidity for 13 months. A survey was conducted to get information about the occurrence of dew condensation and mildew and to investigate residents' satisfaction level with the residential environment. According to the result, all the housing units under study had dew condensation and mildew. The average summer temperature of the 10 housing units was $27.84^{\circ}C$, and average humidity was 64.91%RH, while the average winter temperature was $20.6^{\circ}C$, and the average humidity was 40.12%RH. Depending on the condition of each housing unit, deviation was big. Residents' average level of satisfaction with the living condition was 2.03 which shows a low satisfaction (on a scale of 1-5 with 5 being completely satisfied.).

• Smart Structures and Systems
• /
• 제26권1호
• /
• pp.23-33
• /
• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• Imaging Science in Dentistry
• /
• 제46권1호
• /
• pp.33-38
• /
• 2016

Purpose: Facial asymmetry has been measured by the severity of deviation of the menton (Me) on posteroanterior (PA) cephalograms and three-dimensional (3D) computed tomography (CT). This study aimed to compare PA cephalograms and 3D CT regarding the severity of Me deviation and the direction of the Me. Materials and Methods: PA cephalograms and 3D CT images of 35 patients who underwent orthognathic surgery (19 males and 16 females, with an average age of $22.1{\pm}3.3years$) were retrospectively reviewed in this study. By measuring the distance and direction of the Me from the midfacial reference line and the midsagittal plane in the cephalograms and 3D CT, respectively, the x-coordinates ($x_1$ and $x_2$) of the Me were obtained in each image. The difference between the x-coordinates was calculated and statistical analysis was performed to compare the severity of Me deviation and the direction of the Me in the two imaging modalities. Results: A statistically significant difference in the severity of Me deviation was found between the two imaging modalities (${\Delta}x=2.45{\pm}2.03mm$, p<0.05) using the one-sample t-test. Statistically significant agreement was observed in the presence of deviation (k=0.64, p<0.05) and in the severity of Me deviation (k=0.27, p<0.05). A difference in the direction of the Me was detected in three patients (8.6%). The severity of the Me deviation was found to vary according to the imaging modality in 16 patients (45.7%). Conclusion: The measurement of Me deviation may be different between PA cephalograms and 3D CT in some patients.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2006년도 추계학술대회 논문집(제1권)
• /
• pp.31-36
• /
• 2006

우리나라의 경우 선박조종시뮬레이션을 이용한 선박 통항의 안전성 평가는 흔히 목표점 혹은 목표선 분석으로 이뤄지고 있다. 이 논문에서는 선박조종시뮬레이션 결과에 대하여 목표점 혹은 목표선 분석과 연속분석을 각각 실시하고 이들을 비교 ${\codt}$ 검토하여 각각에 대한 유효성을 제시하고 아울러 각각의 선박의 위치에서 항로 중심에서의 거리가 아닌, 항로경계까지의 최근접거리를 근접도 평가의 한 방법으로 제시하였다.

• Wind and Structures
• /
• 제20권6호
• /
• pp.763-791
• /
• 2015

"Wind and Ports" is a European project that has been carried out since 2009 to handle wind forecast in port areas through an integrated system made up of an extensive in-situ wind monitoring network, the numerical simulation of wind fields, the statistical analysis of wind climate, and algorithms for medium-term (1-3 days) and short term (0.5-2 hours) wind forecasting. The in-situ wind monitoring network, currently made up of 22 ultrasonic anemometers, provides a unique opportunity for detecting high resolution thunderstorm records and studying their dominant characteristics relevant to wind engineering with special concern for wind actions on structures. In such a framework, the wind velocity of thunderstorms is firstly decomposed into the sum of a slowly-varying mean part plus a residual fluctuation dealt with as a non-stationary random process. The fluctuation, in turn, is expressed as the product of its slowly-varying standard deviation by a reduced turbulence component dealt with as a rapidly-varying stationary Gaussian random process with zero mean and unit standard deviation. The extraction of the mean part of the wind velocity is carried out through a moving average filter, and the effect of the moving average period on the statistical properties of the decomposed signals is evaluated. Among other aspects, special attention is given to the thunderstorm duration, the turbulence intensity, the power spectral density and the integral length scale. Some noteworthy wind velocity ratios that play a crucial role in the thunderstorm loading and response of structures are also analyzed.

• 한국재료학회지
• /
• 제13권4호
• /
• pp.233-238
• /
• 2003

Ferromagnetic tunnel junctions, Ta/Cu/Ta/NiFe/Cu/$Mn_{75}$ $Ir_{25}$ $Co_{70}$ $Fe_{30}$/Al-oxide, were fabricated by do magnetron sputtering and plasma oxidation process. The effect of annealing temperature on the local transport properties of the ferromagnetic tunnel junctions was studied using contact-mode Atomic Force Microscopy (AFM). The current images reflected the distribution of the barrier height determined by local I-V analysis. The contrast of the current image became more homogeneous and smooth after annealing at $280^{\circ}C$. And the average barrier height $\phi_{ave}$ increased and its standard deviation $\sigma_{\phi}$ X decreased. For the cases of the annealing temperature more than $300^{\circ}C$, the contrast of the current image became large again. And the average barrier height $\phi_{ave}$ decreased and its standard deviation $\sigma_{\phi}$ increased. Also, the current histogram had a long tail in the high current region and became asymmetric. This result means the generation of the leakage current that is resulted from the local generation of a low barrier height region. In order to obtain the high tunnel magnetoresistance(TMR) ratio, the increase of the average barrier height and the decrease of the barrier height fluctuation must be strictly controlled.led.

• Wind and Structures
• /
• 제36권4호
• /
• pp.237-247
• /
• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.