• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• Computers and Concrete
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• v.33 no.2
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• pp.175-193
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• 2024

Researchers have embarked on an active investigation into the feasibility of adopting alternative materials as a solution to the mounting environmental and economic challenges associated with traditional concrete-based construction materials, such as reinforced concrete. The examination of concrete's mechanical properties using laboratory methods is a complex, time-consuming, and costly endeavor. Consequently, the need for models that can overcome these drawbacks is urgent. Fortunately, the ever-increasing availability of data has paved the way for the utilization of machine learning methods, which can provide powerful, efficient, and cost-effective models. This study aims to explore the potential of twelve machine learning algorithms in predicting the tensile strength of geopolymer concrete (GPC) under various curing conditions. To fulfill this objective, 221 datasets, comprising tensile strength test results of GPC with diverse mix ratios and curing conditions, were employed. Additionally, a number of unseen datasets were used to assess the overall performance of the machine learning models. Through a comprehensive analysis of statistical indices and a comparison of the models' behavior with laboratory tests, it was determined that nearly all the models exhibited satisfactory potential in estimating the tensile strength of GPC. Nevertheless, the artificial neural networks and support vector regression models demonstrated the highest robustness. Both the laboratory tests and machine learning outcomes revealed that GPC composed of 30% fly ash and 70% ground granulated blast slag, mixed with 14 mol of NaOH, and cured in an oven at 300°F for 28 days exhibited superior tensile strength.

• Steel and Composite Structures
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• v.52 no.3
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• pp.293-312
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• 2024

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.623-631
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• 2000

Advanced Life Support Systems (ALSS) are being studied to support human life during long-duration space missions. ALSS can be categorized into four subsystems: Crew, Biomass Production, Food Processing and Nutrition, Waste Processing and Resource Recovery. The System Studies and Modeling (SSM) team of New Jersey-NASA Specialized Center of Research and Training (NJ-NSCORT) has facilitated and conducted analyses of ALSS to address systems level issues. The underlying concept of the SSM work is to enable the effective utilization of information to aid in planning, analysis, design, management, and operation of ALSS and their components. Analytical tools and computer models for ALSS analyses have been developed and implemented for value-added information processing. The results of analyses have been delivered through the Internet for effective communication within the advanced life support (ALS) community. Several modeling paradigms have been explored by developing tools for use in systems analysis. They include object-oriented approach for top-level models, procedural approach for process-level models, and application of commercially available modeling tools such as MATLAB$\^$(R)//Simulink$\^$(R)/. Every paradigm has its particular applicability for the purpose of modeling work. An overview is presented of the systems studies and modeling work conducted by the NJ-NSCORT SSM team in its efforts to provide systems analysis capabilities to the ALS community. The experience gained and the analytical tools developed from this work can be extended to solving problems encountered in general agriculture.

• Journal of Korean Society of Steel Construction
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• v.18 no.3
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• pp.339-347
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• 2006

This study proposes a double-composite section to enhance the s serviceability of twin-girder railway bridges, especially in terms of the flexural stiffness of the composite section in negative-moment regions. This paper deals with experiments on continuous twin-girder bridge models with 5m-5m span length with the proposed double-composite action. From results of static tests on the bridge models, several design considerations were investigated including effective width, shear connection and ultimate strength of the double-composite concrete slab showed full shear connection, which verified the suggested empirical equation. From the flexural behavior of the double-composite section, the effective width of the bottom concrete slab can be evaluated as that of the concrete slab under compression. The ultimate flexural strength of the bridge models verified the validity of the rigid plastic analysis of the double-composite section. Design guidelines were suggested based on the test results.

• Agricultural and Biosystems Engineering
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• v.2 no.2
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• pp.41-49
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• 2001

Advanced Life Support Systems(ALSS) are being studied to support human life during long-duration space missions. ALSS can be categorized into four subsystems: Crew, Biomass Production, Food Processing and Nutrition, Waste Processing and Resource Recovery. The System Studies and Modeling (SSM) team of New Jersey-NASA Specialized Center of Research and Training (NJ-NSCORT) has facilitated and conducted analyses of ALSS to address systems level issues. The underlying concept of the SSM work is to enable the effective utilization of information to aid in planning, analysis, design, management, and operation of ALSS and their components. Analytical tools and computer models for ALSS analyses have been developed and implemented for value-added information processing. The results of analyses heave been delivered through the internet for effective communication within the advanced life support (ALS) community. Several modeling paradigms have been explored by developing tools for use in systems analysis. they include objected-oriented approach for top-level models, procedureal approach for process-level models, and application of commercially available modeling tools such as $MATLAB^{R}$/$Simulink^{R}$. Every paradigm has its particular applicability for the purpose of modeling work. an overview is presented of the systems studies and modeling work conducted by the NJ-NSCORT SSM team in its efforts to provide systems analysis capabilities to the ALS community. The experience gained and the analytical tools developed from this work can be extended to solving problems encountered in general agriculture.

• Journal of the Korea Convergence Society
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• v.6 no.5
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• pp.115-122
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• 2015

The social welfare facilities are trusted by the government for the development of appropriate welfare services and their delivery systems. They aim to provide people with professional social welfare services and, for this purpose, they develop ways to effectively operate facilities and to deliver the services efficiently. So far, the five consecutive evaluations based on the Social Services Act have suggested the effective and systematic ways of operating social welfare facilities. Through these efforts, they established an important basis for the balanced development and further advancement of social welfare facilities. Nevertheless, people have continually pointed out problems concerning the evaluation system of social welfare facilities. Accordingly, these problems have the necessity of investigation for reformed directions by means of effective control and evaluation system for the operation of social welfare facilities. Therefore, this study aims to suggest convergence models for improving the social welfare facilities evaluation system.

• Structural Engineering and Mechanics
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• v.37 no.2
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• pp.149-162
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• 2011

The aim of this research is a comprehensive review and evaluation of beam theories resting on elastic foundations that used to model mode-I delamination in multidirectional laminated composite by DCB specimen. A compliance based approach is used to calculate critical strain energy release rate (SERR). Two well-known beam theories, i.e. Euler-Bernoulli (EB) and Timoshenko beams (TB), on Winkler and Pasternak elastic foundations (WEF and PEF) are considered. In each case, a closed-form solution is presented for compliance versus crack length, effective material properties and geometrical dimensions. Effective flexural modulus ($E_{fx}$) and out-of-plane extensional stiffness ($E_z$) are used in all models instead of transversely isotropic assumption in composite laminates. Eventually, the analytical solutions are compared with experimental results available in the literature for unidirectional ($[0^{\circ}]_6$) and antisymmetric angle-ply ($[{\pm}30^{\circ}]_5$, and $[{\pm}45^{\circ}]_5$) lay-ups. TB on WEF is a simple model that predicts more accurate results for compliance and SERR in unidirectional laminates in comparison to other models. TB on PEF, in accordance with Williams (1989) assumptions, is too stiff for unidirectional DCB specimens, whereas in angle-ply DCB specimens it gives more reliable results. That it shows the effects of transverse shear deformation and root rotation on SERR value in composite DCB specimens.

• Journal of Biomedical Engineering Research
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• v.23 no.5
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• pp.375-383
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• 2002

Aneurysm embolisation method using coils have been widely used. Micro coils are introduced via a small catheter, and are packed inside of aneurysm sac, which induces intraaneurysmal flow stagnation and thrombus formation. When partial blocking of an aneurysm is inevitable, the location of coils is important since it changes the flow patterns inside the aneurysm, which affect the embolisation process. We measured the flow field inside the partially blocked lateral aneurysm models in vitro, and tried to suggest the effective locations of coils for aneurysm embolisation. Velocity fields are measured using a particle image velocitimeter for different coil locations- proximal neck, distal neck, proximal dome and distal dome. Flow into the aneurysm sac was significantly reduced in the distally blocked models, and coils at distal neck blocked inflow more effectively comparing to those at distal dome. This study suggests that distal neck should be the most effective location for aneurysm embolisation.

• Journal of Soil and Groundwater Environment
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• v.19 no.3
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• pp.123-133
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• 2014

It is important to predict the groundwater level fluctuation for effective management of groundwater monitoring system and groundwater resources. In the present study, three different time series models for the prediction of groundwater level in response to rainfall were built, those are transfer function noise model (TFNM), artificial neural network (ANN), and adaptive neuro fuzzy interference system (ANFIS). The models were applied to time series data of Boen, Cheolsan, and Hongcheon stations in National Groundwater Monitoring Network. The result shows that the model performance of ANN and ANFIS was higher than that of TFNM for the present case study. As lead time increased, prediction accuracy decreased with underestimation of peak values. The performance of the three models at Boen station was worst especially for TFNM, where the correlation between rainfall and groundwater data was lowest and the groundwater extraction is expected on account of agricultural activities. The sensitivity analysis for the input structure showed that ANFIS was most sensitive to input data combinations. It is expected that the time series model approach and results of the present study are meaningful and useful for the effective management of monitoring stations and groundwater resources.