• Water for future
• /
• v.21 no.4
• /
• pp.389-397
• /
• 1988

For determination of the design wves at the seven selected sites in the South Sea, a method of hindcasting the past annual largest significant waves from the records of both the wind speed at the nearby weather stations and the weather charts of typhoons are utilized. The design significant waves in deep water are determined through the extremal probability analysis for three major wave directions(SW, S, SE) at each site from the annual extremal series of wave heights. Design significant wave heights with the return period of 100 years ranged between 4.6m and 8.8m with the wave period ranging between 8.2 seconds and 12.9 seconds. Through the analysis of weather maps, both the fetches for the wind directions SW-SE along the South Coast and the relationship between the wind speed at sea and the wind speed at the nearby land weather stations for seasonal winds are determined. The wind speed at sea are found to be 0.8-0.9 times the wind speed at the land stations for $U_1$>15m/s. The ratio of the duration-averaged wind speed to the maximum wind speed varies between 0.7-0.9 as a negative exponential function for the duration ranging 2< t< 13 hours.

• Journal of electromagnetic engineering and science
• /
• v.12 no.4
• /
• pp.290-292
• /
• 2012

This paper proposes a setup for a best multisine design method that uses a time-domain optimization. The method is based on minimization of the time-domain error, so its resulting multisine has a very accurate ACLR estimation. This is because its probability distribution and sample-to-sample correlation are close to those of the original signal, which are crucial for the testing of nonlinear power amplifiers. In addition, a hyperbolic-tangent function is introduced to control the ripple of tone magnitudes within signal bandwidth. For the verification, multisines were generated and compared for many aspects such as normalized error, in-band ripple, and ACLR estimation. Test results with different numbers of tones provide supporting evidence that the suggested multisine design has better ripple suppression, by up to 7 dB, and better accuracy, by up to 0.2 dB, when compared to the conventional method. The accuracy of the ACLR was improved by about 5 dB when the number of tones was 4. The suggested method improves the ACLR estimation performance of multisine testing due to its closer resemblance to the target modulation signal.

• Proceedings of the KSRS Conference
• /
• v.2
• /
• pp.717-720
• /
• 2006

In this study, we predicted locations vulnerable to ground subsidence hazard using fuzzy logic and geographic information system (GIS). Test was carried out at an abandoned underground coal mine in Samcheok City, Korea. Estimation of relative ratings of eight major factors influencing subsidence and determination of effective fuzzy operators are presented. Eight major factors causing ground subsidence were extracted and constructed as a spatial database using the spatial analysis and the probability analysis functions. The eight factors include geology, slope, landuse, depth of mined tunnel, distance from mined tunnel, RMR, permeability, and depth of ground water. A frequency ratio model was applied to calculate relative rating of each factor, and the ratings were integrated using fuzzy membership function and five different fuzzy operators to produce a ground subsidence susceptibility map. The ground subsidence susceptibility map was verified by comparing it with the existing ground subsidences. The obtained susceptibility map well agreed with the actual ground subsidence areas. Especially, ${\gamma}-operator$ and algebraic product operator were the most effective among the tested fuzzy operators.

• Geomechanics and Engineering
• /
• v.25 no.1
• /
• pp.17-30
• /
• 2021

This paper develops a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers. The proposed method is comprised of two major steps, i.e., construction of the training dataset and determination of instance-based classifiers. In step one, the orthogonal design is utilized to produce representative samples after the ranges and levels of the factors that influence tunnel face stability are specified. The training dataset is then labeled by two-dimensional strength reduction analyses embedded within OptumG2. For any unknown instance, the second step applies the training dataset for classification, which is achieved by an ad hoc Python program. The classification of unknown samples starts with selection of instance-based training samples using the k-nearest neighbors algorithm, followed by the construction of an instance-based SVM-KNN classifier. It eventually provides labels of the unknown instances, avoiding calculate its corresponding performance function. Probabilistic evaluations are performed by Monte Carlo simulation based on the SVM-KNN classifier. The ratio of the number of unstable samples to the total number of simulated samples is computed and is taken as the failure probability, which is validated and compared with the response surface method.

• Geomechanics and Engineering
• /
• v.34 no.6
• /
• pp.597-609
• /
• 2023

One of the applicable methods for the stabilization of soil walls is the nailing system which consists of tensile struts. The stability and safety of soil nail wall systems are influenced by the geometrical parameters of the nailing system. Generally, the determination of nailing parameters in order to achieve optimal performance of the nailing system for the safety of soil walls is defined in the framework of optimization problems. Also, according to the various uncertainty in the mechanical parameters of soil structures, it is necessary to evaluate the reliability of the system as a probabilistic problem. In this paper, the optimal design of the nailing system is carried out in deterministic and probabilistic cases using meta-heuristic and reliability-based design optimization methods. The colliding body optimization algorithm and first-order reliability method are used for optimization and reliability analysis problems, respectively. The objective function is defined based on the total cost of nails and safety factors and reliability index are selected as constraints. The mechanical properties of the nailing system are selected as design variables and the mechanical properties of the soil are selected as random variables. The results show that the reliability of the optimally designed soil nail system is very sensitive to uncertainty in soil mechanical parameters. Also, the design results are affected by uncertainties in soil mechanical parameters due to the values of safety factors. Reliability-based design optimization results show that a nailing system can be designed for the expected level of reliability and failure probability.

• Journal of the Korean Society for Railway
• /
• v.20 no.5
• /
• pp.569-576
• /
• 2017

The electromagnetic valve of pneumatic doors of EMUs has a high failure rate due to air leakage because it supplies air on and off to operate the doors repeatedly. The electromagnetic valve is a very important safety component for which a very high reliability is required because failure makes it impossible to operate the passenger cars. However, domestic urban railway operators maintain electronic valves of the EMU door under a fixed cycle with a spare period according to the full overhaul cycle of the EMU. An improvement of the current maintenance cycle was suggested based on the reliability function and mission reliability. Using the statistical program MINITAB for the operational data of EMU line 6, we analyzed the characteristics of the fault distribution and derived the shape and scale parameters of the reliability function. If we limit the specific reliability probability to under a certain failure rate and calculate its statistical parameters, we can calculate the allowable inspection period with mission reliability. Through this study, we suggested a maintenance period based on RCM (reliability centered-maintenance) to improve the reliability of electromagnetic valves from 68% to 95%.

• Asian Journal of Atmospheric Environment
• /
• v.10 no.2
• /
• pp.67-79
• /
• 2016

The forecasting of air pollution is an important and popular topic in environmental engineering. Due to health impacts caused by unacceptable particulate matter (PM) levels, it has become one of the greatest concerns in metropolitan cities like Karaj City in Iran. In this study, the concentration of $PM_{2.5}$ was predicted by applying a multilayer percepteron (MLP) neural network, a radial basis function (RBF) neural network and a Markov chain model. Two months of hourly data including temperature, NO, $NO_2$, $NO_x$, CO, $SO_2$ and $PM_{10}$ were used as inputs to the artificial neural networks. From 1,488 data, 1,300 of data was used to train the models and the rest of the data were applied to test the models. The results of using artificial neural networks indicated that the models performed well in predicting $PM_{2.5}$ concentrations. The application of a Markov chain described the probable occurrences of unhealthy hours. The MLP neural network with two hidden layers including 19 neurons in the first layer and 16 neurons in the second layer provided the best results. The coefficient of determination ($R^2$), Index of Agreement (IA) and Efficiency (E) between the observed and the predicted data using an MLP neural network were 0.92, 0.93 and 0.981, respectively. In the MLP neural network, the MBE was 0.0546 which indicates the adequacy of the model. In the RBF neural network, increasing the number of neurons to 1,488 caused the RMSE to decline from 7.88 to 0.00 and caused $R^2$ to reach 0.93. In the Markov chain model the absolute error was 0.014 which indicated an acceptable accuracy and precision. We concluded the probability of occurrence state duration and transition of $PM_{2.5}$ pollution is predictable using a Markov chain method.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.17 no.3
• /
• pp.178-187
• /
• 2005

A reliability analysis model f3r studying the influence of joint distribution of wave heights and periods on wave un-up is presented in this paper. From the definition of failure mode related to wave run-up, a reliability function may be formulated which can be considered uncertainties of water level. In particular, the reliability analysis model can be directly taken into account statistical properties and distributions of wave periods by considering wave period in the reliability function to be a random variable. Also, variations of wave height distribution conditioned to mean wave periods can be taken into account correctly. By comparison of results of additional reliability analysis using extreme distributions with those resulted from joint distribution of wave height and periods, it is found that probabilities of failure evaluated by the latter is larger than those by the former. Although the freeboard of sloped-breakwater structures can be determined by extreme distribution based on the long-term measurements, it may be necessary to investigate additionally into wave run-up by using the present reliability analysis model formulated to consider joint distribution of a single storm event. In addition, it may be found that the effect of spectral bandwidth parameter on reliability index may be little, but the effect of wave height distribution conditioned to mean wave periods is straightforward. Therefore, it may be confirmed that effects of wave periods on the probability of failure of wave run-up may be taken into account through the conditional distribution of wave heights. Finally, the probabilities of failure with respect to freeboard of sloped-breakwater structures can be estimated by which the rational determination of crest level of sloped-breakwater structures may be possible.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.8 no.2
• /
• pp.123-133
• /
• 2010

A statistical evaluation methodology was developed to determine the compliance of candidate waste stream with clearance criteria based upon distribution of radionuclide in a waste stream at a certain confidence level. For the cases where any information on the radionuclide distribution is not available, the relation between arithmetic mean of radioactivity concentration and its acceptable maximum standard deviation was demonstrated by applying widely-known Markov Inequality and One-side Chebyshev Inequality. The relations between arithmetic mean and its acceptable maximum standard deviation were newly derived for normally or lognormally distributed radionuclide in a waste stream, using probability density function, cumulative density function, and other statistical relations. The evaluation methodology was tested for a representative case at 95% of confidence level and 100 Bq/g of clearance level of radioactivity concentration, and then the acceptable range of standard deviation at a given arithmetic mean was quantitatively shown and compared, by varying the type of radionuclide distribution. Furthermore, it was statistically demonstrated that the allowable range of clearance can be expanded, even at the same confidence level, if information on the radionuclide distribution is available.

• The Journal of Bigdata
• /
• v.5 no.2
• /
• pp.53-67
• /
• 2020

This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.