• Advances in nano research
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• v.12 no.5
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.337-342
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• 2014

This paper is aims to build the advancement structural model of GICOMS through identification of required system and improvement for implementation of e-Navigation. We derived nine improvement subject for model of advanced GICOMS through the analysis of problems for GICOMS and brainstorming with expert in the maritime safety. And we analyzed the structure of nine improvement subject using by FSM(Fuzzy Structural Modeling) method, and proposed a structural model that to grasp the correlation between elements. As a result, we found out that "advancement of GICOMS" is the final goal, and "improvement a system of information production", "improvement a scheme of information providing", "linkage between GICOMS and VTS" and "building global networks for safety cooperation" are located lowest level. Especially, "advancement of GICOMS" is influenced by "advancement function of VMS" and "Activation of usage" on middle level. We suggested that utilizing state-of-the-art IT facilities, equipment and expertise to improve and enhance the user-centered transition such as maritime workers for advancement of GICOMS based on proposed structure model.

• Journal of Korea Water Resources Association
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• v.38 no.7 s.156
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• pp.565-574
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• 2005

It is used water quality data that was measured at Pyeongchanggang real time monitoring stations in Namhan river. These characteristics were analyzed with the water qualify of rainy and nonrainy periods. TOC (Total Organic Carbon) data of rainy periods has correlation with discharge and shows high values of mean, maximum, and standard deviation. DO (Dissolved Oxygen) value of rainy periods is lower than those of nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water qualify forecasting models were applied. LMNN, MDNN, and ANFIS models have achieved the highest overall accuracy of TOC data. LMNN (Levenberg-Marquardt Neural Network) and MDNN (MoDular Neural Network) model which are applied for DO forecasting shows better results than ANFIS (Adaptive Neuro-Fuzzy Inference System). MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. The observation of discharge and water quality are effective at same point as well as same time for real time management. But there are some of real time water quality monitoring stations far from the T/M water stage. Pyeongchanggang station is one of them. So discharge on Pyeongchanggang station was calculated by developed runoff neural network model, and the water quality forecasting model is linked to the runoff forecasting model. That linked model shows the improvement of waterquality forecasting.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.75-88
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• 2024

It would be advantageous to predict acreage and yield of crops in major grain-exporting countries, which would improve decisions on policy making and grain trade in Korea. A climate suitability model can be used to assess crop acreage and yield in a region where the availability of observation data is limited for the use of process-based crop models. The objective of this study was to determine the climate suitability index of wheat by province in Ukraine, which would allow for the spatial assessment of acreage and yield for the given crop. In the present study, the official data of wheat acreage and yield were collected from the State Statistics Service of Ukraine. The EarthStat data, which is a data product derived from satellite data and official crop reports, were also gathered for the comparison with the map of climate suitability index. The Fuzzy Union model was used to create the climate suitability maps under the historical climate conditions for the period from 1970 to 2000. These maps were compared against actual acreage and yield by province. It was found that the EarthStat data for acreage and yield of wheat differed from the corresponding official data in several provinces. On the other hand, the climate suitability index obtained using the Fuzzy Union model explained the variation in acreage and yield at a reasonable degree. For example, the correlation coefficient between the climate suitability index and yield was 0.647. Our results suggested that the climate suitability index could be used to indicate the spatial distribution of acreage and yield within a region of interest.

• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.540-549
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• 2003

In collaborative recommendation two models are generally used: the user model and the article model. A user model learns correlation between users preferences and recommends an article based on other users preferences for the article. Similarly, an article model learns correlation between preferences for articles and recommends an article based on the target user's preference for other articles. In this paper, we investigates various combination methods of the user model and the article model for better recommendation performance. They include simple sequential and parallel methods, perceptron, multi-layer perceptron, fuzzy rules, and BKS. We adopt the multi-layer perceptron for training each of the user and article models. The multi-layer perceptron has several advantages over other methods such as the nearest neighbor method and the association rule method. It can learn weights between correlated items and it can handle easily both of symbolic and numeric data. The combined models outperform any of the basic models and our experiments show that the multi-layer perceptron is the most efficient combination method among them.

• Smart Structures and Systems
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• v.19 no.2
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• pp.127-136
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• 2017

Fiber reinforced polymer (FRP) has received widespread attention in the field of civil engineering because of its superior durability and corrosion resistance. This article presents the damage mechanisms of a novelty composite called carbon nanofiber modified flax fiber polymer (CNF-modified FFRP). The ability of acoustic emission (AE) to detect damage evolution for different configurations of specimens under uniaxial tension was examined, and some useful AE characteristic parameters were obtained. Test results shows that the mechanical properties of modified composites are associated with the CNF content and the evenness of CNF dispersed in the epoxy matrix. Various damage mechanisms was established by means of scanning electron microscope images. The fuzzy c-means clustering were proposed to classify AE events into groups representing different generation mechanisms. The classifiers are constructed using the traditional AE features -- six parameters from each burst. Amplitude and peak-frequency were selected as the best cluster-definition features from these AE parameters. After comprehensive comparison, a correlation between these AE events classes and the damage mechanisms observed was proposed.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.156-161
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• 2001

Currently, agricultural facilities are evaluated using either basic inspections or detailed analysis. However, conventional analyses as well as methods based on fuzzy logic and rule of thumb have not been very successful in providing a clear relationship between rating and real state of agricultural facilities, because they can't provide exactly acceptable reliability of degraded structures with manager or supervisor. Therefore, in this stage, we must define probabilistic variables for representing degradation of structures being given damages during a survival time. This paper describes the application of neural network system in developing the relation between subjective ratings and parameters of agricultural reservoir as well as that between subjective and analytical ratings. It is shown that neural networks can be trained and used successfully in estimating a rating based on several parameters. The specific application problem for agricultural reservoir in the rural area of Korea is presented and database is constructed to maintain training data set, the information of inspection and facilities. This study showed that a successful training of a neural network could be useful, especially if the input data set for target problem contains parameters with a diverse combination of inter-correlation coefficients. And the networks had a prediction rating of about $^{\ast}^{\ast}^{\ast}%$. The neural network system is expected to show high performance fairly in estimate than statistical method to use equation that is consisted of very lowly interrelated variables.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.3
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• pp.605-611
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• 2001

In order to offer the service information of high-quality, we should develop the software of high-Quality. We need the united estimate method, because existing developed quality measurements individually measure attributes using the different viewpoint. Therefor, this paper propose the model that able to include many method of measurements. Our model selects the ratio scales and calculates the relative significance of them by using rough logic. Then, in order to measure the quality of module, it integrates the significance of scales and the measured value of them by using fuzzy integral. Finally, we analyze the correlation between the existing scales with our measurement and validate our model through statistical technique.

• Journal of Distribution Science
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• v.13 no.4
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• pp.9-19
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• 2015

Purpose - This study focuses on the use of evaluative criteria software for imprecise market information, and product mapping relationships between design parameters and customer requirements. Research design, data, and methodology - This study involved using the product predicted value method, synthesizing design alternatives through a morphological analysis and plan, realizing the synthesis in multi-criteria decision-making (MCDM), and using its searching software capacity to obtain optimal solutions. Results - The establishment of product designs conforms to the customer demand, and promotes the optimization of several designs. In this study, the construction level analytic method and the simple multi attribute comment, or the quantity analytic method are used. Conclusions - This study provides a solution for enterprise products' multi-goals decision-making, because the product design lacks determinism, complexity, risk, conflict, and so on. In addition, the changeable factor renders the entire decision-making process more difficult. It uses Fuzzy deduction and the correlation technology for appraising the feasible method and multi-goals decision-making, to solve situations of the products' multi-goals and limited resources, and assigns resources for the best product design.