• Earthquakes and Structures
• /
• 제8권1호
• /
• pp.57-76
• /
• 2015

The Performance-based Earthquake Engineering (PBEE) concept implies the definition of multiple target performance levels of damage which are expected to be achieved (or not exceeded), when the structure is subjected to earthquake ground motion of specified intensity. These levels are associates to different return period (RP) of earthquakes and structural behaviors quantified with adopted factors or indexes of control. In this work an 8-level PBEE study is carried out, finding different curves for control index or Engineering Demand Parameters (EDP) of levels that assess the structural behavior. The results and the curves for each index of control allow to deduce the structural behavior at an a priori unspecified RP. A general methodology is proposed that takes into account a possible optimization process in the PBEE field. Finally, an application to 8-level seismic performance assessment to structure in a Spanish seismic zone permits deducing that its behavior is deficient for high seismic levels (RP > 475 years). The application of the methodology to a low-to-moderate seismic zone case proves to be a good tool of structural seismic design, applying a more sophisticated although simple PBEE formulation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권12호
• /
• pp.5765-5781
• /
• 2018

Extreme learning machine (ELM) is emerging as a powerful machine learning method in a variety of application scenarios due to its promising advantages of high accuracy, fast learning speed and easy of implementation. However, how to select the optimal hidden layer of ELM is still an open question in the ELM community. Basically, the number of hidden layer nodes is a sensitive hyperparameter that significantly affects the performance of ELM. To address this challenging problem, we propose to adopt multiple kernel learning (MKL) to design a multi-hidden-layer-kernel ELM (MHLK-ELM). Specifically, we first integrate kernel functions with random feature mapping of ELM to design a hidden-layer-kernel ELM (HLK-ELM), which serves as the base of MHLK-ELM. Then, we utilize the MKL method to propose two versions of MHLK-ELMs, called sparse and non-sparse MHLK-ELMs. Both two types of MHLK-ELMs can effectively find out the optimal linear combination of multiple HLK-ELMs for different classification and regression problems. Experimental results on seven data sets, among which three data sets are relevant to classification and four ones are relevant to regression, demonstrate that the proposed MHLK-ELM achieves superior performance compared with conventional ELM and basic HLK-ELM.

• 산업경영시스템학회지
• /
• 제43권4호
• /
• pp.208-216
• /
• 2020

With the spread of smart manufacturing, one of the key topics of the 4th industrial revolution, manufacturing systems are moving beyond automation to smartization using artificial intelligence. In particular, in the existing automatic machining, a number of machining defects and non-processing occur due to tool damage or severe wear, resulting in a decrease in productivity and an increase in quality defect rates. Therefore, it is important to measure and predict tool life. In this paper, ν-ASVR (ν-Asymmetric Support Vector Regression), which considers the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, was proposed and applied to the tool wear prediction problem. In the case of tool wear, if the predicted value of the tool wear amount is smaller than the actual value (under-estimation), product failure may occur due to tool damage or wear. Therefore, it can be said that ν-ASVR is suitable because it is necessary to overestimate. It is shown that even when adjusting the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, the ratio of the number of data belonging to ⲉ-tube can be adjusted with ν. Experiments are performed to compare the accuracy of various kernel functions such as linear, polynomial. RBF (radialbasis function), sigmoid, The best result isthe use of the RBF kernel in all cases

• 대한원격탐사학회지
• /
• 제37권1호
• /
• pp.57-67
• /
• 2021

An earthquake occurred on 17 November 2017 in Pohang, South Korea with a strength of 5.4 Mw. This is the second strongest earthquake recorded by local authorities since the equipment was first installed. In order to improve understanding of earthquakes and surface deformation, many studies have been conducted according to these phenomena. In this research, we will estimate the surface deformation using the Okada model equation. The SAR images of three satellites with different wavelengths (ALOS-2, Cosmo SkyMed and Sentinel-1) were used to produce the interferogram pairs. The interferogram is used as a reference for surface deformation changes by using Okada to determine the source of surface deformation that occurs during an earthquake. The Non-linear optimization (Levemberg-Marquadrt algorithm) and Monte Carlo restart was applied to optimize the fault parameter on modeling process. Based on the modeling results of each satellite data, the fault geometry is ~6 km length, ~2 km width and ~5 km depth. The root mean square error values in the surface deformation model results for Sentinel, CSK and ALOS are 0.37 cm, 0.79 cm and 1.47 cm, respectively. Furthermore, the results of this modeling can be used as learning material in understanding about seismic activity to minimize the impacts that arise in the future.

• Smart Structures and Systems
• /
• 제26권6호
• /
• pp.753-763
• /
• 2020

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

• 시스템엔지니어링학술지
• /
• 제16권2호
• /
• pp.67-77
• /
• 2020

Traditionally nuclear thermal hydraulic and nuclear safety has relied on numerical simulations to predict the system response of a nuclear power plant either under normal operation or accident condition. However, this approach may sometimes be rather time consuming particularly for design and optimization problems. To expedite the decision-making process data-driven models can be used to deduce the statistical relationships between inputs and outputs rather than solving physics-based models. Compared to the traditional approach, data driven models can provide a fast and cost-effective framework to predict the behavior of highly complex and non-linear systems where otherwise great computational efforts would be required. The objective of this work is to develop an AI algorithm to predict the peak fuel cladding temperature as a metric for the successful implementation of FLEX strategies under extended station black out. To achieve this, the model requires to be conditioned using pre-existing database created using the thermal-hydraulic analysis code, MARS-KS. In the development stage, the model hyper-parameters are tuned and optimized using the talos tool.

• 우주기술과 응용
• /
• 제2권2호
• /
• pp.67-80
• /
• 2022

최근의 우주개발기술 선진국들은 우주의 평화적 이용이라는 보편적 가치를 넘어서 자국의 이익을 극대화하기 위한 발판으로 우주의 전략적 활용에 더욱 더 집중하고 있으며, 조기경보 위성과 같이 지상에서 발사된 화염 정보를 이용하여 우주로 발사된 발사체의 실시간 감시와 궤적 추적 기능 등을 담당하는 위성들을 지속적으로 개발해 오고 있다. 본 연구에서는 이러한 조기경보 위성에서 발사체의 궤적을 실시간으로 추정할 수 있는 알고리즘을 진화연산이라는 인공지능 기법을 적용하여 제안하고, 이러한 비행 궤적 추정 알고리즘을 비행 궤적 추정 시스템의 시뮬레이터를 통하여 임의의 발사체 비행 궤적에 적용함으로써 제안된 방법의 성능과 특징을 입체적으로 확인하고자 한다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2022년도 학술발표회
• /
• pp.28-28
• /
• 2022

본 연구는 용담댐유역을 포함한 금강 유역 상류 지역을 대상으로 Sentinel-1 SAR (Synthetic Aperture Radar) 위성영상을 기반으로 한 토양수분 산정을 목적으로 하였다. Sentinel-1 영상은 2019년에 대해 12일 간격으로 수집하였고, 영상의 전처리는 SNAP (SentiNel Application Platform)을 활용하여 기하 보정, 방사 보정 및 Speckle 보정을 수행하여 VH (Vertical transmit-Horizontal receive) 및 VV (Vertical transmit-Vertical receive) 편파 후방산란계수로 변환하였다. 토양수분 산정에는 Water Cloud Model (WCM)이 활용되었으며, 모형의 식생 서술자(Vegetation descriptor)는 RVI (Radar Vegetation Index)와 NDVI (Normalized Difference Vegetation Index)를 활용하였다. RVI는 Sentinel-1 영상의 VH 및 VV 편파자료를 이용해 산정하였으며, NDVI는 동기간에 대해 10일 간격으로 수집된 Sentinel-2 MSI (MultiSpectral Instrument) 위성영상을 활용하여 산정하였다. WCM의 검정 및 보정은 한국수자원공사에서 제공하는 10 cm 깊이의 TDR (Time Domain Reflectometry) 센서에서 실측된 6개 지점의 토양수분 자료를 수집하여 수행하였으며, 매개변수의 최적화는 비선형 최소제곱(Non-linear least square) 및 PSO (Particle Swarm Optimization) 알고리즘을 활용하였다. WCM을 통해 산정된 토양수분은 피어슨 상관계수(Pearson's correlation coefficient)와 평균제곱근오차(Root mean square error)를 활용하여 검증을 수행할 예정이다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권11호
• /
• pp.3204-3217
• /
• 2023

In this paper, the downlink of the multicast based spatial modulation systems is investigated. Specifically, physical layer multicasting is introduced to increase the number of access users and to improve the communication rate of the spatial modulation system in which only single radio frequency chain is activated in each transmission. To minimize the bit error rate (BER) of the multicast based spatial modulation system, a joint optimizing algorithm of antenna selection and multicast precoding is proposed. Firstly, the joint optimization is transformed into a mixed-integer non-linear program based on single-stage reformulation. Then, a novel iterative algorithm based on the idea of branch and bound is proposed to obtain the quasioptimal solution. Furthermore, in order to balance the performance and time complexity, a low-complexity deflation algorithm based on the successive convex approximation is proposed which can obtain a sub-optimal solution. Finally, numerical results are showed that the convergence of our proposed iterative algorithm is between 10 and 15 iterations and the signal-to-noise-ratio (SNR) of the iterative algorithm is 1-2dB lower than the exhaustive search based algorithm under the same BER accuracy conditions.

• 대한핵의학회지
• /
• 제35권3호
• /
• pp.152-160
• /
• 2001

목적: 게이트 심근 SPECT를 이용하여 좌심실심근의 최대탄성률을 측정하는 방법의 재현성을 조사하고 단일 압력-부피 고리방법과 매개변수 최적화 방법으로 얻었을 때 재현성의 차이를 조사하였다. 대상 및 방법: 관상동맥질환이 의심되는 47명 (남자 42명, 여자 5명, 평균나이 53세, 좌심실구혈률 22-68%)에서 Tc-99m MIBI게이트 심근 SPECT와 동시에 요골동맥 긴장도를 측정하였다. 이 중 11명에서는 누운 자리에서 움직이지 않고 연이어 두번 측정하였다. 최대탄성률과 영점부피를 Lee 등이 제안한 단일 압력-부피 고리 방법과 Yoshizawa 등이 제안한 선형근사에 의한 매개변수 최적화 방법으로 추정하였다. 결과: 최대탄성률(상관계수 0.96)과 영점부피(상관계수 0.99)의 재현성은 단일 압력-부피 고리 방법이 매개변수 최적화 방법(최대탄성률 0.89, 영점부피 0.64)보다 좋았다. 두 방법으로 추정한 최대탄성률의 상관계수는 0.77, 영점부피의 상관계수는 0.65이었다. 결론: 게이트 심근 SPECT와 동맥긴장도 측정법으로 얻어 단일 압력-부피 고리 방법으로 추정한 좌심실 심근의 최대탄성률의 재현성은 매우 우수하였다. 매개변수 최적화 방법으로 얻은 최대탄성률도 우수하나 단일 압력-부피 고리 방법보다 못하였다.