• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.259-259
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• 2020

증발산량은 수문학적으로는 강수량으로부터 지표 유출량과 지하수 함양량을 추정하는 등 전체 물수지를 해석하는데 있어서 매우 중요하며, 농업적 측면에서는 작물의 용수 수요량을 결정하는 중요한 인자이다. 그러나 증발산량의 직접적인 계측이 쉽지 않기 때문에 물수지 방법에 의한 간접적인 추정이나 관련된 기상자료를 이용한 경험적이고 물리적인 해석을 통해 산정하고 있다. 일반적으로 특정조건의 작물(기준작물)을 기준으로 가용수분이 충분한 상태에서 주어진 기상조건에 대해 기준증발산량을 산정하며, 여기에 대상작물별 특성이나 토양의 실제수분상태 등을 고려하여 실제증발산량을 추정하고 있다. 본 연구에서는 한강권역을 대상으로 현재 가장 일반적으로 활용되고 있는 Penman-Monteith 방법을 비롯하여, Thornthwaite 방법, Hamon 방법, Priestly-Taylor 방법, Hargreaves-Samani 방법, Hansen 방법 등 총 6종의 기준증발산량을 산정하여 비교하였다. 각 방법에 필요한 기상자료는 한강권역 및 인근에 위치한 기상청 관할의 33개 ASOS 지점에 대한 60년간(1960~2019년)의 관측자료를 이용하였다. Penman-Monteith 방법에 의한 값을 기준으로 나머지 5가지 방법들에 의한 결과를 분석한 결과, 전반적으로 다른 방법들이 기준증발산량을 크게 산정하는 것으로 나타났으며, temperature-based 접근법인 Hamon과 Hargreaves-Samani에 의한 연평균 값은 Penman-Monteith 방법 대비 각각 28.5%, 19.3% 정도 크게 산정되었다. 특히 Hamon 방법에 의한 결과는 다른 방법과 비교하여 여름철에 크게 차이를 보였다. 반면 Hansen 방법은 상대적으로 Penmna-Monteith 방법과 가장 적은 편차를 나타내었다. 지역별로 분석했을 때는 서울/인천지역과 강원도 동해안 지역을 제외하고는 Penman-Monteith 방법 대비 다른 방법들의 기준증발산량이 큰 것으로 나타났다. 중권역별로는 Penman-Monteith 결과와 비교하여 -158 mm/yr 에서 최대 +307 mm/yr 정도의 편차를 나타내었으며, 월별로는 -13 mm에서 +73 mm의 편차가 나타났다.

• Computers and Concrete
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• 제32권1호
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• pp.99-117
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• 2023

The crack under the influence of the higher intensities of the stresses grows and the structure gets collapsed with the time when the crack length reaches to critical value. Therefore, the fracture behavior of a structure in terms of stress intensity factors (SIF) becomes important to determine the remaining fracture strength and capacity of material and structure for avoiding catastrophic failure, increasing safety and further improvement in the design. The robustness of the method has been demonstrated by comparing the numerical results with analytical and experimental results of some problems. XFEM is used to model cracks and holes in structures and predict their strength and reliability under service conditions. Further, XFEM is extended with a stochastic method for predicting the sensitivity in terms of output COVs and fracture strength in terms of mean values of stress intensity factors (SIFs) of a structure with discontinuities (cracks and holes) under tensile loading condition with input individual and combined randomness in different system parameters. In stochastic technique, the second order perturbation technique (SOPT) has been used for the predicting the fracture behavior of the structures. The stochastic/perturbation technique is also known as Taylor series expansion method and it provides the reliable results if the input randomness is less than twenty percentage. From the present numerical analysis it is observed that, the crack tip near to the hole is under the influence of the stress concentration and the variational effect of the input random parameters on the crack tip in terms of the SIFs are lesser so the COVs are the less sensitive. The COVs of mixed mode SIFs are the most sensitive for the crack angles (α=45° to 90°) for all the values of c₁ and d₁. The plate with the shorter distance between hole and crack is the most sensitive with all the crack angles but the crack tip which is much nearer to the hole has the highest sensitivity.

• 영어영문학
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• 제56권5호
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• pp.973-998
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• 2010

This paper aims at assessing the significance of Lyrical Ballads of 1798, the agreed inaugurator of English Romanticism, in terms of such key concepts as poetic "experiments," "conversation," and the authorial function. The 1798 volume marks an interesting incidence in which an author with no tangible substantiality can wield his authorial function over his works. The volume is signed without the named proper noun-its author is neither William Wordsworth nor Samuel Taylor Coleridge. The figure of the author in this case is realized by the poems he writes; he produces, and is produced by, his works-a fact that constitutes part of the poetic experiments manifested in the Advertisement. Working under this reciprocal production, the Author of the 1798 volume and his poems are collectively aiming at establishing a new class of poetry and an interpretive community. The notion of "conversation" is a key element in the thematic, stylistic ties among individual poems. Poems of the 1798 volume effect multi-layered, "blended" voices. Readers are expected to draw out the topological interweaving among poems through the practices of dialogic reading. In this light, the sequential necessity of "The Rime" and "Tintern Abbey" should be emphasized. They are stitched together in a logic of textual placement and the transition from one to the other is never arbitrary. Most of all, they are working under the same authorial function, complementing each other, and addressing the same poetic project in different textual locations. As an inaugural work of English Romanticism, Lyrical Ballads of 1798 in fact makes so many things happen and yet again anticipates something yet to come with elusiveness. The value of this poetic experiments should be judged not only by what is claimed in it, but what it sets out to do and "how far" it will be performed, as implied in the Advertisement. The efficacy of the volume, more than anything else, is dependent upon the performative power of words.

• Geomechanics and Engineering
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• 제31권2호
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• 한국인터넷방송통신학회논문지
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• 제23권5호
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• pp.97-102
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• 2023

본 논문에서는 두 개의 수신안테나를 이용한 저가 레이더를 구현하기 위한 송수신 안테나를 제작하고 그 특성을 분석하였다. 안테나는 수평방향으로의 빔 집중과 저가격을 위해 MPA(Microstrip Patch Array) 구조로 설계하였으며 사이드로브(sidelobe)를 억제하기 위해 테일러 배열 패턴 합성을 이용하였다. 측정 결과 사용대역인 24GHz ISM 밴드(24.0-24.25GHz) 내에서 안테나 이득이 15.2-16.26 dBi를 나타내어 설계 스펙인 15dBi이상 17dBi이하의 조건을 만족하였다. 사이드로브는 동작 주파수가 24.0 GHz, 24.125 GHz, 24.25 GHz일 때 각각 -13.15 dBc, -13.1 dBc, 및 -12.8 dBc로서 -10.0 dBc이하의 스펙을 만족하였다.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.179-187
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• 2023

The outbreak of COVID-19 had affected almost every part of the world and caused disastrous results, the number of reported COVID-19 cases in past few months have reached to more than 29 million patients in the world globally. This pandemic has adversely affected all the activities of life, ranging from personal life to overall economic development. Due to the current situation, routinely turned to online resources, and people have relied on technology more than they have been before. Since cybercriminals are an opportunist and they utilized this entirely, by targeting the online services for all sectors of life. This fortnight online dependency of the community over the internet opened several easy doors for the cybercriminals. This causes exponential attacks over internet traffic during this epidemic situation. The current Covid-19 pandemic situation appeared at once, and no one was ready to prevail this. However, there is an urgent need to address the current problem in all means. . KSA is among one of the countries most affected by these CA and is a key victim for most cyber-crimes. Therefore, this paper will review the effects of COVID-19 on the cyber-world of KSA in various sectors. We will also shed light on the Saudi efforts to confront these attacks during COVID -19. As a contribution, we have provided a comprehensive framework for mitigating cybersecurity challenges.

• Computers and Concrete
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• 제32권6호
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.43-52
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• 2024

Medical imaginings assume a important part in the analysis of tumors and cerebrospinal fluid (CSF) leak. Magnetic resonance imaging (MRI) is an image segmentation technology, which shows an angular sectional perspective of the body which provides convenience to medical specialists to examine the patients. The images generated by MRI are detailed, which enable medical specialists to identify affected areas to help them diagnose disease. MRI imaging is usually a basic part of diagnostic and treatment. In this research, we propose new techniques using the 4D-MRI image segmentation process to detect the brain tumor in the skull. We identify the issues related to the quality of cerebrum disease images or CSF leakage (discover fluid inside the brain). The aim of this research is to construct a framework that can identify cancer-damaged areas to be isolated from non-tumor. We use 4D image light field segmentation, which is followed by MATLAB modeling techniques, and measure the size of brain-damaged cells deep inside CSF. Data is usually collected from the support vector machine (SVM) tool using MATLAB's included K-Nearest Neighbor (KNN) algorithm. We propose a 4D light field tool (LFT) modulation method that can be used for the light editing field application. Depending on the input of the user, an objective evaluation of each ray is evaluated using the KNN to maintain the 4D frequency (redundancy). These light fields' approaches can help increase the efficiency of device segmentation and light field composite pipeline editing, as they minimize boundary artefacts.

• Computers and Concrete
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• 제33권1호
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Advances in materials Research
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• 제13권1호
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• pp.63-86
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• 2024

Asphalt concrete (AC), is a mixture of bitumen and aggregates, which is very sensitive in the design of flexible pavement. In this study, the Marshall stability of the glass and carbon fiber bituminous concrete was predicted by using Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and M5P Tree machine learning algorithms. To predict the Marshall stability, nine inputs parameters i.e., Bitumen, Glass and Carbon fibers mixed in 100:0, 75:25, 50:50, 25:75, 0:100 percentage (designated as 100GF:0CF, 75GF:25CF, 50GF:50 CF, 25GF:75CF, 0GF:100CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD) were utilized from the experimental and literary data. Seven statistical indices i.e., coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), Scattering index (SI), and BIAS were applied to assess the effectiveness of the developed models. According to the performance evaluation results, Artificial neural network (ANN) was outperforming among other models with CC values as 0.9147 and 0.8648, MAE values as 1.3757 and 1.978, RMSE values as 1.843 and 2.6951, RAE values as 39.88 and 49.31, RRSE values as 40.62 and 50.50, SI values as 0.1379 and 0.2027 and BIAS value as -0.1 290 and -0.2357 in training and testing stage respectively. The Taylor diagram (testing stage) also confirmed that the ANN-based model outperforms the other models. Results of sensitivity analysis showed that the fiber length is the most influential in all nine input parameters whereas the fiber combination of 25GF:75CF was the most effective among all the fiber mixes in Marshall stability.