• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1131-1142
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• 2018

The autonomous vehicles are being developed and operated widely because of the advantages of reducing the traffic accident and saving time and cost for driving. The vehicle localization is an essential component for autonomous vehicle operation. In this paper, localization algorithm based on sensor fusion is developed for cost-effective localization using in-vehicle sensors, GNSS, an image sensor and reference images that made in advance. Information of the reference images can overcome the limitation of the low positioning accuracy that occurs when only the sensor information is used. And it also can acquire estimated result of stable position even if the car is located in the satellite signal blockage area. The particle filter is used for sensor fusion that can reflect various probability density distributions of individual sensors. For evaluating the performance of the algorithm, a data acquisition system was built and the driving data and the reference image data were acquired. Finally, we can verify that the vehicle positioning can be performed with an accuracy of about 0.7 m when the route image and the reference image information are integrated with the route path having a relatively large error by the satellite sensor.

• Computers and Concrete
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• v.23 no.6
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• pp.433-444
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• 2019

As rice husk ash (RHA) is not produced in controlled manufacturing process like cement, its properties vary significantly even within the same lot. In fact, properties of Rice Husk Ash Based Concrete (RHABC) are largely dictated by uncertainty leading to huge deviations from their expected values. This paper proposes a Robust Cost Optimization (RCO) procedure for RHABC, which minimizes such unwanted deviation due to uncertainty and provides guarantee of achieving desired strength and workability with least possible cost. The RCO simultaneously minimizes cost of RHABC production and its deviation considering feasibility of attaining desired strength and workability in presence of uncertainty. RHA related properties have been modeled as uncertain-but-bounded type as associated probability density function is not available. Metamodeling technique is adopted in this work for generating explicit expressions of constraint functions required for formulation of RCO. In doing so, the Moving Least Squares Method is explored in place of conventional Least Square Method (LSM) to ensure accuracy of the RCO. The efficiency by the proposed MLSM based RCO is validated by experimental studies. The error by the LSM and accuracy by the MLSM predictions are clearly envisaged from the test results. The experimental results show good agreement with the proposed MLSM based RCO predicted mix properties. The present RCO procedure yields RHABC mixes which is almost insensitive to uncertainty (i.e., robust solution) with nominal deviation from experimental mean values. At the same time, desired reliability of satisfying the constraints is achieved with marginal increment in cost.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.109-115
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• 2019

The probability of lethality of personnel targets inside a room is a key issue at assessing effectiveness of a weapon system. In this study, the minimum charge weight to achieve 100% lethality of personnel targets inside a box-type room is proposed at each side length of a base of a room. A fast running blast wave model is used to simulate the pressure-time histories of the blast generated by an internal explosion inside a room, and Axelsson SP method is used to evaluate the lethality of personnel targets under the blast. 176 different internal explosion scenarios are simulated for cases of TNT weights ranging from 20kg to 170kg inside a room whose square base has a side length ranging from 5m to 15m. A linear model and a charge-density model were developed to predict the minimum charge weight to achieve 100% lethality inside a room given a length of a base of a room.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.995-1002
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• 2020

This paper investigates the achievable data rate for non-orthogonal multiple access(NOMA) with correlated information sources(CIS), under the binary phase shift keying(BPSK) modulation, in contrast to most of the existing NOMA designs using continuous Gaussian input modulations. First, the closed-form expression for the achievable data rate of NOMA with CIS and BPSK is derived, for both users. Then it is shown by numerical results that for the stronger channel user, the achievable data rate of CIS reduces, compared with that of independent information sources( IIS). We also demonstrate that for the weaker channel user, the achievable data rate of CIS increases, compared with that of IIS. In addition, the intensive analyses of the probability density function(PDF) of the observation and the inter-user interferennce(IUI) are provided to verify our theoretical results.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.75-84
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• 2021

As the weather changes become frequent, weather disasters are increasing, causing more damage to plastic greenhouses. Among the damage caused by various disasters, damage by snow to the greenhouse takes a relatively long time, so if an alarm system is properly prepared, the damage can be reduced. Existing greenhouse design standards and snow warning systems are based on snow depth. However, even in the same depth, the load on the greenhouse varies depending on meteorological characteristics and snow density. Therefore, this study aims to secure the structural safety of greenhouses by developing sensors that can directly measure snow loads, and analysing the warning criteria for load using a stochastic model. Markov chain was applied to estimate the failure probability of various types of greenhouses in various regions, which let users actively cope with heavy snowfall by selecting an appropriate time to respond. Although it was hard to predict the precise snow depth or amounts, it could successfully assess the risk of structures by directly detecting the snow load using the developed sensor.

• Animal Bioscience
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• v.35 no.4
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• pp.511-516
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• 2022

Objective: A genomic region associated with a particular phenotype is called quantitative trait loci (QTL). To detect the optimal F₂ population size associated with QTLs in native chicken, we performed a simulation study on F₂ population derived from crosses between two different breeds. Methods: A total of 15 males and 150 females were randomly selected from the last generation of each F₁ population which was composed of different breed to create two different F₂ populations. The progenies produced from these selected individuals were simulated for six more generations. Their marker genotypes were simulated with a density of 50K at three different heritability levels for the traits such as 0.1, 0.3, and 0.5. Our study compared 100, 500, 1,000 reference population (RP) groups to each other with three different heritability levels. And a total of 35 QTLs were used, and their locations were randomly created. Results: With a RP size of 100, no QTL was detected to satisfy Bonferroni value at three different heritability levels. In a RP size of 500, two QTLs were detected when the heritability was 0.5. With a RP size of 1,000, 0.1 heritability was detected only one QTL, and 0.5 heritability detected five QTLs. To sum up, RP size and heritability play a key role in detecting QTLs in a QTL study. The larger RP size and greater heritability value, the higher the probability of detection of QTLs. Conclusion: Our study suggests that the use of a large RP and heritability can improve QTL detection in an F₂ chicken population.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1846-1852
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• 2021

In modern society, various digital devices are being distributed in a wide range of fields due to the fourth industrial revolution and the development of IoT technology. However, noise is generated in the process of acquiring or transmitting an image, and not only damages the information, but also affects the system, causing errors and incorrect operation. AWGN is a representative noise among image noise. As a method for removing noise, prior research has been conducted, and among them, AF, A-TMF, and MF are the representative methods. Existing filters have a disadvantage that smoothing occurs in areas with high frequency components because it is difficult to consider the characteristics of images. Therefore, the proposed algorithm calculates the standard deviation distribution to effectively eliminate noise even in the high frequency domain, and then calculates the final output by applying the probability density function weight of the Laplace distribution using the curve fitting method.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.765-773
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• 2022

This study is a study on the application of a horizontal excavation machine system to improve constructability. In this study, the structural stability of non-covered temporary facilities was evaluated by comparing field measurements and numerical analysis. In addition, the appropriateness of the measurement results was analyzed by comparing and analyzing the results of numerical analysis with the analysis results applying the Gaussian probability density function to the measurement results. In this study, structural safety and long-term durability of the linkage were analyzed based on numerical analysis. As a result of the study, it was analyzed that the non-open cut method (H.A.S. method) of this study secures structural safety and constructability as the behavior in the actual construction process is more safe than the numerical analysis results, even if the uncertainty of the ground condition is taken into account.

• Women's Health Nursing
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• v.28 no.2
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• pp.112-122
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• 2022

Purpose: The prevalence of osteoporosis in postmenopausal women is increasing, and diabetes patients have decreased bone density. Their health-related quality of life (HRQoL) is diminished by the resultant physical dysfunction and depression. The purpose of this study was to identify factors influencing HRQoL in postmenopausal women with diabetes and osteoporosis. Methods: This was a secondary data analysis of the Seventh Korea Health and Nutrition Examination Survey (2016-2018), which utilized a complex, multistage probability sample design. The participants in the study were 237 women with diabetes and osteoporosis. To evaluate the factors that influenced HRQoL, a complex-samples general linear model was constructed, and the Bonferroni correction was performed. Results: In this sample of women aged 45 to 80 years (mean±standard deviation, 71.12±7.21 years), the average HRQoL score was 0.83±0.18 out of 1.0. Factors influencing HRQoL were age (70s: t=-3.74, p<.001; 80s: t=-3.42, p=.001), walking for exercise more than 5 days a week (t=-2.83, p=.005), cerebrovascular disease (t=-8.33, p<.001), osteoarthritis (t=-2.04, p=.014), hypertension (t=2.03, p=.044), higher perceived stress (t=-2.17, p=.032), poor glycemic control (t=3.40, p=.001), waist circumference (t=-2.76, p=.007), sitting time per day (t=-2.10, p=.038), and a longer postmenopausal period (t=3.09, p=.002). Conclusion: In order to improve the HRQoL of postmenopausal women with osteoporosis and diabetes, it is necessary to implement intervention strategies that enable the effective management of chronic diseases, while preventing the complications of diabetes and minimizing stress through physical activity.