• International conference on construction engineering and project management
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• 2015.10a
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• pp.656-657
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• 2015

Recently, Multi-Objective Optimization of design elements is an important issue in building design. Design variables that considering the specificities of the different environments should use the appropriate algorithm on optimization process. The purpose of this study is to compare and analyze the optimal solution using three evolutionary algorithms and energy modeling simulation. This paper consists of three steps: i)Developing three evolutionary algorithm model for optimization of design elements ; ii) Conducting Multi-Objective Optimization based on the developed model ; iii) Conducting comparative analysis of the optimal solution from each of the algorithms. Including Non-dominated Sorted Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO) and Random Search were used for optimization. Each algorithm showed similar range of result data. However, the execution speed of the optimization using the algorithm was shown a difference. NSGA-II showed the fastest execution speed. Moreover, the most optimal solution distribution is derived from NSGA-II.

• Smart Structures and Systems
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• v.23 no.2
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• pp.107-122
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• 2019

As one of the most important parameters in structural health monitoring, structural frequency has many advantages, such as convenient to be measured, high precision, and insensitive to noise. In addition, frequency-change-ratio based method had been validated to have the ability to identify the damage occurrence and location. However, building a precise enough finite elemental model (FEM) for the test structure is still a huge challenge for this frequency-change-ratio based damage detection technique. In order to overcome this disadvantage and extend the application for frequencies in structural health monitoring area, a novel method was developed in this paper by combining the cross-model cross-mode (CMCM) model updating algorithm with the frequency-change-ratio based method. At first, assuming the physical parameters, including the element mass and stiffness, of the test structure had been known with a certain value, then an initial to-be-updated model with these assumed parameters was constructed according to the typical mass and stiffness distribution characteristic of shear buildings. After that, this to-be-updated model was updated using CMCM algorithm by combining with the measured frequencies of the actual structure when no damage was introduced. Thus, this updated model was regarded as a representation of the FEM model of actual structure, because their modal information were almost the same. Finally, based on this updated model, the frequency-change-ratio based method can be further proceed to realize the damage detection and localization. In order to verify the effectiveness of the developed method, a four-level shear building was numerically simulated and two actual shear structures, including a three-level shear model and an eight-story frame, were experimentally test in laboratory, and all the test results demonstrate that the developed method can identify the structural damage occurrence and location effectively, even only very limited modal frequencies of the test structure were provided.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.279-286
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• 2005

The objective of this study is to suggest a credit scoring model of a capital company's customers by integration of multiple classifiers using genetic algorithm. For this purpose , an integrated model is derived in two phases. In first phase, three types of classifiers MLP (Multi-Layered Perceptron), RBF (Radial Basis Function) and linear models - are trained, in which each type has three ones respectively so htat we have nine classifiers totally. In second phase, genetic algorithm is applied twice for integration of classifiers. That is, after htree models are derived from each group, a final one is from these three, In result, our suggested model shows a superior accuracy to any single ones.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.733-739
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• 2012

In this paper, a smoke detection algorithm robust to brightness and color variations depending on time and weather is proposed. The proposed smoke detection algorithm specifies the candidate region using difference images of input and background images, determines smoke by comparing feature coefficients of Gaussian mixture model of difference images. Thresholds for specifying candidate region is divided by four levels according to average brightness and chrominance of input images. Clusters of Gaussian mixture models of difference images are aligned according to average brightness. Smoke is determined by comparing distance of Gaussian mixture model parameters. The proposed algorithm is implemented by media dedicated DSP. As results of experiments, it is shown that the proposed algorithm is effective to detect smoke with camera installed outdoor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.3
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• pp.492-498
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• 2010

Since it is almost impossible to train the dispatchers with real power system, the dispatcher training simulator(DTS) is used for the training. Among various components of the DTS, the power system model(PSM) emulates the dynamic behavior of the power system to calculate the frequency and voltage. The frequency is calculated from various parameters such as mechanical power of power plants, load, inertia, and the damping of the power system. In the PSM, the power plants are modeled as differential equations, so the mechanical power of the power plants are calculated by the numerical methods. Conventionally, the fixed step-size algorithm has been used in the PSM, however it has some drawbacks. This paper develops the prototype PSM using the Matlab, and analyzes the problems of the fixed step-size algorithm by comparing the results with those of PSCAD simulation. In order to overcome the limitations, this paper proposes a modified frequency calculation method using the adaptive step-size algorithm. From the simulation using the proposed method, it is verified that the accuracy of frequency calculation increases substantially while the simulation time is not greatly increased.

• Journal of Power Electronics
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• v.10 no.5
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• pp.485-490
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• 2010

Sensorless control methods are generally used in motor control for home-appliances because of the material cost and manufactureing standard restrictions. The current model-based control algorithm is mainly used for PMSM sensorless control in the home-appliance industry. In this control method, the rotor position is estimated by using the d-axis and q-axis current errors between the real system and a motor model of the position estimator. As a result, the accuracy of the motor model parameters are critical in this control method. A mismatch of the PMSM parameters affects the speed and torque in low speed, steadystate responses. Rotor position errors are mainly caused by a mismatch of the stator resistance. In this paper, a stator resistance compensation algorithm is proposed to improve sensorless control performance. This algorithm is easy to implement and does not require a modification of the motor model or any special interruptions of the controller. The effectiveness of the proposed algorithm is verified through experimental results.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1017-1030
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• 2016

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.584-589
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• 2001

This paper presents the dynamic parameter design optimization of a suspension seat system using the genetic algorithm. At first, an equivalent 1-D.O.F. mass-spring-damper model of a suspension seat system was constructed for the purpose of its vibration analysis. Vertical vibration response and transmissibility of the equivalent model due to base excitations, which are defined in the ISO's seat vibration test codes, were computed. Furthermore, seat vibration test, that is ISO's damping test, was carried out in order to investigate the validity of the equivalent suspension seat model. Both analytical and experimental results showed good agreement each other. For the design optimization, the acceleration transmissibility of the suspension seat model was adopted as an object function. A simple genetic algorithm was used to search the optimum values of the design variables, suspension stiffness and damping coefficient. Finally, vibration ride performance test results showed that the optimum suspension parameters gives the lowest vibration transmissibility. Accordingly the genetic algorithm and the equivalent suspension seat modelling can be successfully adopted in the vibration ride quality optimization of a suspension seat system.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.202-208
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• 2015

In this study, we propose a new inference algorithm for a multiclass Gaussian process classification model using a variational EM framework and the Laplace approximation (LA) technique. This is performed in two steps, called expectation and maximization. First, in the expectation step (E-step), using Bayes' theorem and the LA technique, we derive the approximate posterior distribution of the latent function, indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. In the maximization step, we compute the maximum likelihood estimators for hyper-parameters of a covariance matrix necessary to define the prior distribution of the latent function by using the posterior distribution derived in the E-step. These steps iteratively repeat until a convergence condition is satisfied. Moreover, we conducted the experiments by using synthetic data and Iris data in order to verify the performance of the proposed algorithm. Experimental results reveal that the proposed algorithm shows good performance on these datasets.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).