• Geomechanics and Engineering
• /
• v.37 no.1
• /
• pp.65-72
• /
• 2024

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early-stage decision-making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning-based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting-edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.

• Journal of Biomedical Engineering Research
• /
• v.16 no.2
• /
• pp.223-230
• /
• 1995

This study investigates a combined mathematical model for the quantitative estimation of regional myocardial blood flow in experimental canine coronary artery occlusion and in patients with ischemic myocardial diseases using Rb-82 dynamic myocardial positron emission tomography. The coronary thrombosis was induced using the new catheter technique by narrowing the lumen of coronary vessel gradually, which finally led to partial obstruction of coronary artery. Thirty four Rb-82 dynamic myocardial PET scans were performed sequentially for each experiment using our 5, 10 and 20 second acquisition protocol, respectively, and six to seven regions of interest were drawn on each transaxial slices, one on left ventricular chamber for input function and the others on normal and decreased perfusion myocardial segments for the flow estimation in those regions. Two compartment model and graphical analysis method have been applied to the measured sets of regional PET data, and the rate constants of influx to myocardial tissue were calculated for regional myocardial flow estimates with the two parameter fits of raw data by the Levenberg-Marquardt method. The results showed that, (I) two compartment model suggested by Kety-Schmidt, with proper modification of the measured data and volume of distribution, could be used for the simple estimation of regional myocardial blood flow, (2) the calculated regional myocardial blood flow estimates were dependent on the selection of input function, which reflected partial volume effect and left ventricular wall motion in previously used graphical analysis, and (3) mathematically fitted input and tissue time activity curves were more suitable than the direct application of the measured data in terms of convergence.

• Journal of KIISE:Software and Applications
• /
• v.37 no.2
• /
• pp.120-128
• /
• 2010

A hypernetwork is a generalized hypo-graph and a probabilistic graphical model based on evolutionary learning. Hypernetwork models have been applied to various domains including pattern recognition and bioinformatics. Nevertheless, conventional hypernetwork models have the limitation that they can manage data with categorical or discrete attibutes only since the learning method of hypernetworks is based on equality comparison of hyperedges with learned data. Therefore, real-valued data need to be discretized by preprocessing before learning with hypernetworks. However, discretization causes inevitable information loss and possible decrease of accuracy in pattern classification. To overcome this weakness, we propose a novel feature-wise L1-distance based method for real-valued attributes in learning hypernetwork models in this study. We show that the proposed model improves the classification accuracy compared with conventional hypernetworks and it shows competitive performance over other machine learning methods.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.1
• /
• pp.99-111
• /
• 2012

The log-density ratio of the conditional densities of the predictors given the response variable provides useful information for variable selection in the logistic regression model. In this paper, we consider the predictors that are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. Under this assumption, linear and log terms are generally included in the model. The log-odds graph is a very useful graphical tool in this study. A graphical study is presented which shows that if the conditional distributions of x|y for the two groups overlap significantly, we need both the linear and quadratic terms. On the contrary, if they are well separated, only the linear or log term is needed in the model.

• The KIPS Transactions:PartD
• /
• v.15D no.4
• /
• pp.505-512
• /
• 2008

In order to do requirement-based testing of embedded system, we must have correct requirement specifications. But, natural language requirements of a client have ambiguity, inaccuracy, and inconsistency. To solve these problems, natural language requirements are modeled with modeling language such as UML and Simulink. During a modeling phase, the requirements are rearranged and retranslated in use-case. These activities are disadvantages of modeling. In this paper, we propose the technique, which is how to model a embedded system requirement into a model without rearranging and retranslating. This technique 1) represent a embedded system requirement with graphical language, and 2) model a requirement into a model. Because this technique only describes "what-to-do" of the requirement, this technique is useful to not only the low-level requirements but also the high-level requirements. We show some example systems modeled by REED, which has adopted this technique.

• Journal of the Ergonomics Society of Korea
• /
• v.18 no.3
• /
• pp.141-152
• /
• 1999

In this paper, We performed the human body dynamic modelling for the realistic animation based on the dynamical behavior of human body, and designed controller for the effective control of complicate human dynamic model. The human body was simplified as a rigid body which consists of 18 actuated degrees of freedom for the real time computation. Complex human kinematic mechanism was regarded as a composition of 6 serial kinematic chains : left arm, right arm, support leg, free leg, body, and head. Based on the this kinematic analysis, dynamic model of human body was determined using Newton-Euler formulation recursively. The balance controller was designed in order to control the nonlinear dynamics model of human body. The effectiveness of designed controller was examined by the graphical simulation of human walking motion. The simulation results were compared with the model base control results. And it was demonstrated that, the balance controller showed better performance in mimicking the dynamic motion of human walking.

• Journal of the Society of Naval Architects of Korea
• /
• v.31 no.4
• /
• pp.23-31
• /
• 1994

The compartmentation model is represented by the characteristics and geometric information of the spaces defined by the structural members which are used for the boundary planes of the compartment. For the efficient compartment modeling a program performing the compartmentation design by the chopping and merging method was presented by authors. In this research, the development of an interactive ship compartmentation modeller is introduced. It is natural that the value of the program lessens if the input process is complicated and uneasy, even though the internal techniques for the compartmentation modeling are superior. In this paper, a method for the convenient input is proposed and implemented with the help of a graphical user interface technique. The modeling method introduced in this paper performs an efficient compartmentation modeling fast and conveniently by the solid modeling concept and the graphical user interface.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.3
• /
• pp.354-362
• /
• 2008

This paper presents the new method of power system transient stability simulation, which combines the desirable features of both the time domain technique based on OOP(Object-oriented Programming) and the direct method of transient stability analysis using detailed generator model. OOP is an alternative to overcome the problems associated with the development, maintenance and update of large software by electrical utilities. Several papers have already evaluated this approach for power system applications in areas such as load flow, security assessment and graphical interface. This paper applied the object-oriented approach to the problem of power system dynamics simulation. The modeling method is that each block of dynamic system block diagram is implemented as an object and connected each other. In the transient energy method, the detailed synchronous generator model is so-called two-axis model. For the excitation model, IEEE type1 model is used. The developed mothed was successfully applied to New England Test System.

• Journal of the Korea Institute of Building Construction
• /
• v.11 no.1
• /
• pp.91-99
• /
• 2011

Bayesian network is a form of probabilistic graphical model. It incorporates human reasoning to deal with sparse data availability and to determine the probabilities of uncertain cases. In this research, bayesian network is adopted to model the problem of construction project cost. General information, time, cost, and material, the four main factors dominating the characteristic of construction costs, are incorporated into the model. This research presents verify a model that were conducted to illustrate the functionality and application of a decision support system for predicting the costs. The Markov Chain Monte Carlo (MCMC) method is applied to estimate parameter distributions. Furthermore, it is shown that not all the parameters are normally distributed. In addition, cost estimates based on the Gibbs output is performed. It can enhance the decision the decision-making process.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2000.10a
• /
• pp.268-271
• /
• 2000

In these days, the technical advances and complexities have generated much of the difficulties in managing the project resources, both time and costing to accomplish the project in the most efficient manner. The project manager is frequently required to render judgements concerning the schedule and resource adjustments. This research develops an analytical model for a schedule-cost and risk analysis based on visual PERT/CPM. We used a two-step approaches ：in the step 1, a deterministic PERT/CPM model for the critical path and estimating the project time schedule and related resource planning, In the second step, we developed a heuristic model for crash and stretch out analysis based upon a time-cost trade-off associated with the crash and stretch out of the project. Computer implementation of this model is provided based on GUI-Type objective-oriented programming for the users and provided displays of all the inputs and outputs in the form of visual graphical. Also developed GUI-type program, Dongeui Visual-PERT/CPM. The results of this research will provide the project managers with an efficient management tool.