• Title/Summary/Keyword: Multi-Fields Model

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LSTM-based Early Fire Detection System using Small Amount Data

  • Seonhwa Kim;Kwangjae Lee
    • Journal of the Semiconductor & Display Technology
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    • v.23 no.1
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    • pp.110-116
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    • 2024
  • Despite the continuous advancement of science and technology, fire accidents continue to occur without decreasing over time, so there is a constant need for a system that can accurately detect fires at an early stage. However, because most existing fire detection systems detect fire in the early stage of combustion when smoke is generated, rapid fire prevention actions may be delayed. Therefore we propose an early fire detection system that can perform early fire detection at a reasonable cost using LSTM, a deep learning model based on multi-gas sensors with high selectivity in the early stage of decomposition rather than the smoke generation stage. This system combines multiple gas sensors to achieve faster detection speeds than traditional sensors. In addition, through window sliding techniques and model light-weighting, the false alarm rate is low while maintaining the same high accuracy as existing deep learning. This shows that the proposed fire early detection system is a meaningful research in the disaster and engineering fields.

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Basic Study on Flame-Stabilization Characteristics in a Multi-Channel Combustor via a Model of a Porous-Media Combustor (다공성 연소기 모델로서의 다중 채널 연소기 내부 화염의 안정화 특성 기초 연구)

  • Park, Seung-Il;Lee, Min-Jung;Kim, Nam-Il
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.35 no.8
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    • pp.815-823
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    • 2011
  • Combustion phenomena in porous media combustors are widely used in industrial fields for the combustion of lowgrade fuels and the regeneration of combustion heat. However, studies of combustion phenomena in porous media have been limited, because these phenomena are difficult to observe, and the configurations of porous media are complex. We propose a simple model combustor: a multi-channel combustor that consists of many layers of combustion channels made of quartz plates. We conducted an experimental observation of the flames in the multi-channel combustor and obtained experimental results for the flame stabilization limits. Flames formulated in the multi-channel combustor showed variation in the spatial distribution depending on the heat transfer between neighboring channels. A simple analytical model was developed and the variation in the flammability limits of the multi-channel combustor was discussed. This study will enhance our understanding of flame behavior in a porous-media combustor.

Combined multi-predict-correct iterative method for interaction between pulsatile flow and large deformation structure

  • Wang, Wenquan;Zhang, Li-Xiang;Yan, Yan;Guo, Yakun
    • Coupled systems mechanics
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    • v.1 no.4
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    • pp.361-379
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    • 2012
  • This paper presents a fully coupled three-dimensional solver for the analysis of interaction between pulsatile flow and large deformation structure. A partitioned time marching algorithm is employed for the solution of the time dependent coupled discretised problem, enabling the use of highly developed, robust and well-tested solvers for each field. Conservative transfer of information at the fluid-structure interface is combined with an effective multi-predict-correct iterative scheme to enable implicit coupling of the interacting fields at each time increment. The three-dimensional unsteady incompressible fluid is solved using a powerful implicit time stepping technique and an ALE formulation for moving boundaries with second-order time accurate is used. A full spectrum of total variational diminishing (TVD) schemes in unstructured grids is allowed implementation for the advection terms and finite element shape functions are used to evaluate the solution and its variation within mesh elements. A finite element dynamic analysis of the highly deformable structure is carried out with a numerical strategy combining the implicit Newmark time integration algorithm with a Newton-Raphson second-order optimisation method. The proposed model is used to predict the wave flow fields of a particular flow-induced vibrational phenomenon, and comparison of the numerical results with available experimental data validates the methodology and assesses its accuracy. Another test case about three-dimensional biomedical model with pulsatile inflow is presented to benchmark the algorithm and to demonstrate the potential applications of this method.

ANALYSIS ON STEAM CONDENSING FLOW USING NON-EQUILIBRIUM WET-STEAM MODEL (비평형 습증기 모델을 적용한 증기 응축 유동 해석)

  • Kim, C.H.;Park, J.H.;Ko, D.G.;Kim, D.I.;Kim, Y.S.;Baek, J.H.
    • Journal of computational fluids engineering
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    • v.20 no.3
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    • pp.1-7
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    • 2015
  • When the steam is used as working fluid in fluid machinery, different from other gases as air, phase transition (steam condensation) can occur and it affects not only the flow fields, but also machine performance & efficiency. Therefore, considering phase transition phenomena in CFD calculation is required to achieve accurate prediction of steam flow and non-equilibrium wet-steam model is needed to simulate realistic steam condensing flow. In this research, non-equilibrium wet-steam model is implemented on in-house code(T-Flow), the flow fields including phase transition phenomena in convergent-divergent nozzle are studied and compared to results of advance researches.

Global Optimization Using Kriging Metamodel and DE algorithm (크리깅 메타모델과 미분진화 알고리듬을 이용한 전역최적설계)

  • Lee, Chang-Jin;Jung, Jae-Jun;Lee, Kwang-Ki;Lee, Tae-Hee
    • Proceedings of the KSME Conference
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    • 2001.06c
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    • pp.537-542
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    • 2001
  • In recent engineering, the designer has become more and more dependent on computer simulation. But defining exact model using computer simulation is too expensive and time consuming in the complicate systems. Thus, designers often use approximation models, which express the relation between design variables and response variables. These models are called metamodel. In this paper, we introduce one of the metamodel, named Kriging. This model employs an interpolation scheme and is developed in the fields of spatial statistics and geostatistics. This class of interpolating model has flexibility to model response data with multiple local extreme. By reason of this multi modality, we can't use any gradient-based optimization algorithm to find global extreme value of this model. Thus we have to introduce global optimization algorithm. To do this, we introduce DE(Differential Evolution). DE algorithm is developed by Ken Price and Rainer Storn, and it has recently proven to be an efficient method for optimizing real-valued multi-modal objective functions. This algorithm is similar to GA(Genetic Algorithm) in populating points, crossing over, and mutating. But it introduces vector concept in populating process. So it is very simple and easy to use. Finally, we show how we determine Kriging metamodel and find global extreme value through two mathematical examples.

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Predicting Potential Epidemics of Rice Leaf Blast Disease Using Climate Scenarios from the Best Global Climate Model Selected for Individual Agro-Climatic Zones in Korea (국내 농업기후지대 별 최적기후모형 선정을 통한 미래 벼 도열병 발생 위험도 예측)

  • Lee, Seongkyu;Kim, Kwang-Hyung
    • Journal of Climate Change Research
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    • v.9 no.2
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    • pp.133-142
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    • 2018
  • Climate change will affect not only the crop productivity but also the pattern of rice disease epidemics in Korea. Impact assessments for the climate change are conducted using various climate change scenarios from many global climate models (GCM), such as a scenario from a best GCM or scenarios from multiple GCMs, or a combination of both. Here, we evaluated the feasibility of using a climate change scenario from the best GCM for the impact assessment on the potential epidemics of a rice leaf blast disease in Korea, in comparison to a multi?model ensemble (MME) scenario from multiple GCMs. For this, this study involves analyses of disease simulation using an epidemiological model, EPIRICE?LB, which was validated for Korean rice paddy fields. We then assessed likely changes in disease epidemics using the best GCM selected for individual agro?climatic zones and MME scenarios constructed by running 11 GCMs. As a result, the simulated incidence of leaf blast epidemics gradually decreased over the future periods both from the best GCM and MME. The results from this study emphasized that the best GCM selection approach resulted in comparable performance to the MME approach for the climate change impact assessment on rice leaf blast epidemic in Korea.

EDMFEN: Edge detection-based multi-scale feature enhancement Network for low-light image enhancement

  • Canlin Li;Shun Song;Pengcheng Gao;Wei Huang;Lihua Bi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.4
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    • pp.980-997
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    • 2024
  • To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.

An evolutionary approach for structural reliability

  • Garakaninezhad, Alireza;Bastami, Morteza
    • Structural Engineering and Mechanics
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    • v.71 no.4
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    • pp.329-339
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    • 2019
  • Assessment of failure probability, especially for a complex structure, requires a considerable number of calls to the numerical model. Reliability methods have been developed to decrease the computational time. In this approach, the original numerical model is replaced by a surrogate model which is usually explicit and much faster to evaluate. The current paper proposed an efficient reliability method based on Monte Carlo simulation (MCS) and multi-gene genetic programming (MGGP) as a robust variant of genetic programming (GP). GP has been applied in different fields; however, its application to structural reliability has not been tested. The current study investigated the performance of MGGP as a surrogate model in structural reliability problems and compares it with other surrogate models. An adaptive Metropolis algorithm is utilized to obtain the training data with which to build the MGGP model. The failure probability is estimated by combining MCS and MGGP. The efficiency and accuracy of the proposed method were investigated with the help of five numerical examples.

Coupling of Electromagnetic and Electrostatic Waves in Inhomogeneous Plasmas

  • Kim, Kyung-Sub;Kim, Eun-Hwa;Lee, Dong-Hun
    • Bulletin of the Korean Space Science Society
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    • 2003.10a
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    • pp.82-82
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    • 2003
  • It is well known that electromagnetic (EM) waves are mode converted to electrostatic (ES) waves in inhomogeneous plasmas. We examine this issue in a three-dimensional multi-fluid numerical model. First, we derive a set of coupled linear wave equations when a one-dimensional inhomogeneous density profile is assumed in a cold and collisionless plasma. The massive ions are considered as fixed because we are interested in high frequency waves in plasmas. It is shown that the EM mode satisfies the 0th order modified Bessel equation near the resonant region where the frequency matches the local electron plasma frequency. It is expected that the EM waves are coupled and damped to the ES waves owing to the logarithmic singular behavior at such resonances. Second, we numerically test the same case in a 3-D multi-fluid model. An impulsive input is assumed to excite EM waves in the inhomogeneous 3-D box model. The wave spectra of electric and magnetic fields are presented and compared with the analytical results. Our results suggest that the EM energy is irreversibly converted into the ES energy wherever the resonant condition is satisfied. Finally we discuss how the mode conversion appears in both electric and magnetic fields by analyzing time histories of each component. We also compare our results with MHD wave coupling. It is numerically confirmed in this study that the coupling of EM and ES waves is similar to that of compressional and transverse MHD waves.

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A Hybrid Method of MultiAttribute Utility Theory and Analytic Hierarchy Process for R&D Projects' Priority Setting. (MAUT/AHP를 이용한 연구개발사업 우선순위 선정방법)

  • 김정흠;박주형
    • Proceedings of the Technology Innovation Conference
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    • 1999.06a
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    • pp.245-265
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    • 1999
  • MAUT and AHP are widely used for quantification of subjective judgements in various fields of decision making. This study focuses on the introduction and application of MAUT/AHP method which is a hybrid of MAUT and AHP techniques in R&D project priority setting. This hybrid model can clarify each factors' contribution using MAUT method and can reduce the number of pairwise comparisons of AHP method. This study applies AMUT/AHP method to the evaluation of R&D projects in a Government - funded research institute. To evaluate R&D projects, six evaluation factors are derived. SMART(Simple MultiAttribute Rating Technique) and DVM(Difference Value Measurement ) out of many MAUT methods are used to design the utility function ad AHP is used to allocate the weights among evaluation factors. The major findings of this study can be summarized as follows. First, the SMART/AHP and the DVM/AHP have the same results with the SMART and the DVM, and they are different results with AHP. It is very hard to decide which one is better. Second, MAUT/AHP's strength is analyzed. MAUT reflects utility values of evaluators to alternatives and AHP results objective and consistent weights of factors through pariwise comparisons. Third, its possible application fields are proposed. It is applicable to subjective decision making problems with high complexity and inter-independent factors.

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