• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.204-209
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• 2013

The image processing has been applied to various fields along with development of visual media. The boundary parts in which brightness of image dramatically changes are important factors in order to analysis characteristics of image because edge contains important information and significant features. A number of researches for detecting these edges have been conducted and conventional edge detection methods using relationship between adjacent pixels are that operation speed is superior, but the edge detection characteristics are insufficient because they use fixed mask without considering gray-level variation. In this paper, the novel algorithm using grey-level variation of image in mask is proposed.

• Journal of Power System Engineering
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• v.22 no.6
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• pp.28-35
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• 2018

The large-eddy simulation (LES) was carried out to evaluate the instantaneous vector and vorticity profiles of a small-size axial fan (SSAF) at the operating point of full-flowrate. The downstream flow of the SSAF exhibits a shorter axial flow when not fully developed, especially the stronger vortex appears at the edge near the flow end. On the other hand, the downstream flow of the SSAF exhibits a longer axial flow, and the weaker vortex appears at the edge near the flow end when the flow is sufficiently developed. Moreover, in the downstream of the SSAF, a periodic and intermittent flow pattern appears at the edge showing the axial flow, and the instantaneous vorticity contour lines showing the form of a circle group are distributed at specific intervals from the downstream region of the blade tip, which is considered to be the result of the intermittency phenomenon influenced by the number of blades and the number of revolutions.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.49-60
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• 2019

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.5
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• pp.271-280
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• 2021

The code developed using a pressure-based method for unified conservation laws of incompressible/compressible fluids is expanded to handle moving or deforming body boundaries using the hybrid Cartesian/immersed boundary method. An instantaneous pressure field is calculated from a pressure Poisson equation for the whole fluid domain, including the compressible gas region. The polytropic gas is assumed for the compressible fluid so that the energy equation is decoupled. Immersed boundary nodes are identified based on edges crossing body boundaries. The velocity vector is reconstructed at the immersed boundary node using an interpolation along the assigned local normal line. The developed code is validated by comparing the time histories of pressure and wave elevation for sloshing in a rectangular and a membrane-type tank. The validated code is applied to simulate air cushion effects in a rectangular tank under sway motion. Time variations of pressure fields are analyzed in detail as the air pocket pulsates. It is shown that the contraction and expansion of the air pocket dominate the pressure loads on the wall of the tank. The present results are in good agreement with other experimental and computational results for the amplitude and the decay of the pressure oscillations measured at the pressure gauges.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.

• Journal of IKEEE
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• v.26 no.3
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• pp.506-509
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• 2022

In a secret sharing scheme, secret information must be distributed and stored to users, and confidentiality must be able to be reconstructed only from an authorized subset of users. To do this, secret information among different code words must not be subordinate to each other. The minimal code is a kind of linear block code to distribute these secret information not mutually dependent. In this paper, we present a novel extension technique for minimal codes. The product of an arbitrary vector and a minimal code produces a new minimal code with an extended length and Hamming weight. Accordingly, it is possible to provide minimal codes with parameters not known in the literature.

• Journal of Korean Society of Rural Planning
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• v.28 no.4
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• pp.105-117
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• 2022

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R² of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.

• Wind and Structures
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• v.36 no.6
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• pp.355-366
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• 2023

Although machine learning (ML) techniques have been widely used in various fields of engineering practice, their applications in the field of wind engineering are still at the initial stage. In order to evaluate the feasibility of machine learning algorithms for prediction of wind loads on high-rise buildings, this study took the exposure category type, wind direction and the height of local wind force as the input features and adopted four different machine learning algorithms including k-nearest neighbor (KNN), support vector machine (SVM), gradient boosting regression tree (GBRT) and extreme gradient (XG) boosting to predict wind force coefficients of CAARC standard tall building model. All the hyper-parameters of four ML algorithms are optimized by tree-structured Parzen estimator (TPE). The result shows that mean drag force coefficients and RMS lift force coefficients can be well predicted by the GBRT algorithm model while the RMS drag force coefficients can be forecasted preferably by the XG boosting algorithm model. The proposed machine learning based algorithms for wind loads prediction can be an alternative of traditional wind tunnel tests and computational fluid dynamic simulations.

• Atmosphere
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• v.33 no.5
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• pp.457-475
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• 2023

Accurately predicting localized heavy rainfall is challenging without high-resolution mesoscale cloud information in the numerical model's initial field, as precipitation intensity and amount vary significantly across regions. In the Korean Peninsula, the radar observation network covers the entire country, providing high-resolution data on hydrometeors which is suitable for data assimilation (DA). During the pre-processing stage, radar reflectivity is classified into hydrometeors (e.g., rain, snow, graupel) using the background temperature field. The mixing ratio of each hydrometeor is converted and inputted into a numerical model. Moreover, assimilating saturated water vapor mixing ratio and decomposing radar radial velocity into a three-dimensional wind vector improves the atmospheric dynamic field. This study presents radar DA experiments using a numerical prediction model to enhance the wind, water vapor, and hydrometeor mixing ratio information. The impact of radar DA on precipitation prediction is analyzed separately for each radar component. Assimilating radial velocity improves the dynamic field, while assimilating hydrometeor mixing ratio reduces the spin-up period in cloud microphysical processes, simulating initial precipitation growth. Assimilating water vapor mixing ratio further captures a moist atmospheric environment, maintaining continuous growth of hydrometeors, resulting in concentrated heavy rainfall. Overall, the radar DA experiment showed a 32.78% improvement in precipitation forecast accuracy compared to experiments without DA across four cases. Further research in related fields is necessary to improve predictions of mesoscale heavy rainfall in South Korea, mitigating its impact on human life and property.

• The Korean Journal of Zoology
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• v.24 no.3
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• pp.151-161
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• 1981

The field studies on the seasonal population prevalences of the vector mosquito larvae and other aquatic invertebrates were weekly carried out in the rice fields located in front of a village of Kwangtan-samri, Byeogje-myeon, Goyang-gun, Gyeonggido throughout mosquito breeding season (June-September) in 1980, and the results are as follows. The population of C. tritaeniorhynchus larvae increased soon after heavy rainfall, and decreased to some extent during the period of the insecticide application. The seasonal prevalence of A. sinensis was rather stable, not being affected by insecticide pressure at all. The population densities of other aquatic invertebrates in rice fields were seriously suppressed by the pesticide application, and their recovery was not as same as the previous level or not at all: (1) The populations of Odonata and Ephemeroptera nymphs were drastically decreased by the first application of insecticides and never recovered through out season. (2) Coleoptera seemed very susceptible to the insecticide application, as densities were markedly decreased whenever the insecticides were applied. (3) The high density of Hemiptera shown in early June were decreased sharply after the first application of pesticides, and thereasfter, some degree of recovery was shown, but suppressed by successive each application of pesticides. (4) A native species of planaria was exceptionally not influenced by the insecticide pressure, and two peaks of the density were appeared during the heavy rainfall.