• International Journal of Ocean System Engineering
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• v.3 no.3
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• pp.116-130
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• 2013

The prediction of ocean currents in real time over the warning times of a few hours or days is required in planning many operation-related activities in the ocean. Traditionally this is done through numerical models which are targeted toward producing spatially distributed information. This paper discusses a complementary method to do so when site-specific predictions are desired. It is based on the use of a recurrent type of neural network as well as the statistical tool of model tree. The measurements made at a site in Indian Ocean over a period of 4 years were used. The predictions were made over 72 time steps in advance. The models developed were found to be fairly accurate in terms of the selected error statistics. Among the two modeling techniques the model tree performed better showing the necessity of using distributed models for different sub-domains of data rather than a unique one over the entire input domain. Typically such predictions were associated with average errors of less than 2.0 cm/s. Although the prediction accuracy declined over longer intervals, it was still very satisfactory in terms of theselected error criteria. Similarly prediction of extreme values matched with that of the rest of predictions. Unlike past studies both east-west and north-south current components were predicted fairly well.

• Korean Journal of Childcare and Education
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• v.18 no.5
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• pp.79-99
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• 2022

Objective: This study was conducted to examine the significant factors affecting media device addiction using the data mining technique for large-scale data from the Panel Study on Korean Children Survey (PSKC). The PSKC data of this study were gathered from the elementary school students in their 10th survey (1,286 3rd grade students). Methods: The SPSS 21.0 program was used for data mining decision tree analysis, and the results are as follows. Results: First, the most important predictor of media device addiction was planning-organization which was among the sub-factors of executive function. Second, as a result of the decision tree analysis, the children with the highest probability of addiction to media devices were ones that had difficulties in planning and organizing, had mothers with a permissive parenting attitude felt difficulties in controlling behavior, and were alone at home for more than two hours a day without any adult supervision. Conclusion/Implications: The results of this study can help guide the direction of future research related to children's addiction to media devices by exploring and analyzing factors that significantly affect children's addiction to media devices.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.303-312
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• 2020

Safety culture is a collection of the beliefs, perceptions and values that employees share in relation to risks within an organisation. On the other hand, a resilient safety culture (RSC) means a culture with readiness of the organisation to respond effectively under stress, bounce back from shocks and continuously learn from them. RSC helps organisations to protect their interest which can be attributed to behavioural, psychological and managerial capabilities of the organization. Quantification of the degree of resilience in an organisation's safety culture can provide insights about the strong and weak links of the organisation's overall health and safety situation by identifying potential causes of system or sub-system failure. One of the major challenges of quantification of RSC is that the attributes that determine RSC need to be measured through constructs and indicators which are complex and often interrelated. In this paper, we address this challenge by applying a fault tree analysis (FTA) technique which can help analyse complex and interrelated constructs and indicators. The fault tree model of RSC is used to evaluate resilience levels of two organisations with remote and urban locations in order to demonstrate the failure path of the weak links in the RSC model.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.1-6
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• 2023

In this paper, we present a new method for classifying malicious URLs to reduce cases of learning difficulties due to unfamiliar and difficult terms related to information protection. This study plans to extract only visually distinguishable features within the URL structure and compare them through map learning algorithms, and to compare the contribution values of the best map learning algorithm methods to extract features that have the most impact on classifying malicious URLs. As research data, Kaggle used data that classified 7,046 malicious URLs and 7.046 normal URLs. As a result of the study, among the three supervised learning algorithms used (Decision Tree, Support Vector Machine, and Logistic Regression), the Decision Tree algorithm showed the best performance with 83% accuracy, 83.1% F1-score and 83.6% Recall values. It was confirmed that the contribution value of https is the highest among whether to use https, sub domain, and prefix and suffix, which can be visually distinguished through the feature contribution of Decision Tree. Although it has been difficult to learn unfamiliar and difficult terms so far, this study will be able to provide an intuitive judgment method without explanation of the terms and prove its usefulness in the field of malicious URL detection.

• The KIPS Transactions:PartA
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• v.17A no.3
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• pp.113-120
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• 2010

Even networks were introduced as a class of fault-tolerant multiprocessor networks and analyzed so many useful properties and algorithms such as simple routing algorithms, maximal fault tolerance, node disjoint path. Introduced routing algorithms and node disjoint path algorithms are proven to be optimal. However, it has not been introduced to constructing scheme for edge-disjoint spanning trees in even networks. The design of edge-disjoint spanning trees is a useful scheme to analyze for measuring the efficiency of fault tolerant of interconnection network and effective broadcasting. Introduced routing algorithm or node disjoint path algorithm are for the purpose of routing or node disjoint path hence they are not applicable to constitute edge disjoint spanning tree. In this paper, we show a construction algorithm of edge-disjoint spanning trees in even network $E_d$.

• The Korean Journal of Physiology and Pharmacology
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• v.22 no.6
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• pp.721-729
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• 2018

GABAergic control over dopamine (DA) neurons in the substantia nigra is crucial for determining firing rates and patterns. Although GABA activates both $GABA_A$ and $GABA_B$ receptors distributed throughout the somatodendritic tree, it is currently unclear how regional GABA receptors in the soma and dendritic compartments regulate spontaneous firing. Therefore, the objective of this study was to determine actions of regional GABA receptors on spontaneous firing in acutely dissociated DA neurons from the rat using patch-clamp and local GABA-uncaging techniques. Agonists and antagonists experiments showed that activation of either $GABA_A$ receptors or $GABA_B$ receptors in DA neurons is enough to completely abolish spontaneous firing. Local GABA-uncaging along the somatodendritic tree revealed that activation of regional GABA receptors limited within the soma, proximal, or distal dendritic region, can completely suppress spontaneous firing. However, activation of either $GABA_A$ or $GABA_B$ receptor equally suppressed spontaneous firing in the soma, whereas $GABA_B$ receptor inhibited spontaneous firing more strongly than $GABA_A$ receptor in the proximal and distal dendrites. These regional differences of GABA signals between the soma and dendritic compartments could contribute to our understanding of many diverse and complex actions of GABA in midbrain DA neurons.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.697-726
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• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.1 s.108
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• pp.71-80
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• 2005

In order to search for ecologically indicative characteristics on the street tree plots in Daegu area, plant communities and their floras were investigated. A total of 105 plots were collected and numerically analyzed by PCoA(Principal Coordinates Analysis). These plots were classified into 4 types containing 139 species, 97 genera, 42 families(including 37 exotic species): urban-dry type, urban-wet type, rural-dry type, rural-wet type. Habitat connectivity to the surrounding vegetation cover, extent and frequency of human impacts, and soil moisture recognizably were the main factors to allow the plots differentiation. Indicative species composition to these four types was generated as Eleusine indica-Eragrostis multicaulis-Oxalis corniculata to the urban-wet, Digitaria ciliaris-Eleusine indica-Eragrostis multicaulis to the urban-dry, Setaria viri-dis-Artemisia-Lactuca indica var. laciniata to the rural-wet, and Setaria viridis-Digitaria ciliaris-Erigeron canadensis to the rural-dry, respectively. Mean species number per plot for rural type was 2.5 times higher than for urban types. Street tree species representative to the rural-wet type is Zelkova serrata, which is a key species of potential natural vegetation in the alluvial land of Daegu area. Street tree plots were characterized by Eleusine indica showing the highest r-NCD value and also C4-assimilation grass plants. Views on the efficacy of the rural-wet type and the reinforcement of vegetation connectivity and soil moisture in rehabilitating street tree plots, are discussed.

• Journal of Forest and Environmental Science
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• v.29 no.1
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• pp.81-89
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• 2013

Nowadays, excessive using of fossil fuel contributes to global warming. Also, this phenomenon increases steadily. Therefore forest biomass from logging residues has received attention. The goal of this study was to determine the sustainability and economic feasibility of forest-biomass energy source. Accordingly, forest biomass resource was calculated, and harvesting and transporting machines which can be used in investing area were chosen, when using forest biomass as energy source. And then through these data, the harvesting cost was decided. The forest biomass resource calculated, thinned trees and logging residues, was 37,330.23 $m^3$ and 14,073.60 ton, respectively. When harvesting timber in each sub-compartment, the average thinned trees yield was 120.73 $m^3$, and tree logging residues was 402.80 ton. The use of tower yarder as harvesting and transporting equipments in study area was 85.4% and 66.7%, respectively, in up hill and down hill yarding. The average harvesting cost of biomass in the possibility area of timber yarding operation was expensive as 81,757 won/$m^3$, 85,434 won/m3 and 50,003 won/ton, respectively, in thinned trees and logging residue. If using data from this research analysis, tree could be felled by choosing sub-compartment.

• Journal of Korean Society of Forest Science
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• v.95 no.2
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• pp.177-182
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• 2006

Little information is available on soil $CO_2$ efflux and crop yield under agroforestry systems. Soil $CO_2$ efflux, microbial biomass C, live fine root biomass, and cotton yield were measured under a pecan (Carya illinoinensis K. Koch)-cotton (Gossypium hirsutum L.) alley cropping system in southern USA. A belowground polyethylene root barrier was used to isolate tree roots from cotton which is to provide barrier and non-barrier treatments. The barrier and non-barrier treatment was randomly divided into three plots for conventional inorganic fertilizer application and the other three plots for organic poultry litter application. The rate of soil $CO_2$ efflux and the soil microbial biomass C were affected significantly (P < 0.05) by the fertilizer treatment while no significant effect of the barrier treatment was occurred. Cotton lint yield was significantly (P < 0.0 I) affected by the root barrier treatment while no effect was occurred by the fertilizer treatment with the yields being greatest ($521.2kg\;ha^{-1}$) in the root barrier ${\times}$ inorganic fertilizer treatment and lowest ($159.8kg\;ha^{-1}$) in the non-barrier ${\times}$ inorganic fertilizer treatment. The results suggest that the separation of tree-crop root systems with the application of inorganic fertilizer influence the soil moisture and soil N availability, which in tum will affect the magnitude of crop yield.