• Smart Structures and Systems
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• 제14권2호
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• pp.145-158
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• 2014

The objective of this study is to improve the survivability and reliability of the FBG sensor network in the structural health monitoring (SHM) system. Therefore, a model reconstruction soft computing recognition algorithm based on support vector regression (SVR) is proposed to achieve the high reliability of the FBG sensor network, and the grid search algorithm is used to optimize the parameters of SVR model. Furthermore, in order to demonstrate the effectiveness of the proposed model reconstruction algorithm, a SHM system based on an eight-point fiber Bragg grating (FBG) sensor network is designed to monitor the foreign-object low velocity impact of a CFRP composite plate. Simultaneously, some sensors data are neglected to simulate different kinds of FBG sensor network failure modes, the predicting results are compared with non-reconstruction for the same failure mode. The comparative results indicate that the performance of the model reconstruction recognition algorithm based on SVR has more excellence than that of non-reconstruction, and the model reconstruction algorithm almost keeps the consistent predicting accuracy when no sensor, one sensor and two sensors are invalid in the FBG sensor network, thus the reliability is improved when there are FBG sensors are invalid in the structural health monitoring system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 한국항해항만학회 2018년도 춘계학술대회
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• pp.246-247
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• 2018

Since the routes within VTS areas include harbour limit of major ports, there are sections where the traffic volume increases and the routes are normally narrow according to the geographical conditions. In the case of ports and VTS areas located on the west coast of Korea, it is affected by strong current due to large tidal differences. In this paper, we propose a method to produce useful information according to the change of navigation environment by analyzing the characteristics of ship's movement according to tidal stream or current. The SVR seaway model, support vector regression, and grid search were conducted in order to extract models.

• Journal of KIISE
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• 제43권1호
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• pp.54-60
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• 2016

Brain gene expression information is closely related to the structural and functional characteristics of the brain. Thus, extensive research has been carried out on the relationship between gene expression patterns and the brain's structural organization. In this study, Principal Component Analysis was used to extract features of gene expression patterns, and genes were automatically classified by spatial distribution. Voxels were then clustered with classified specific region expressed genes. Finally, we visualized the clustering results for mouse hippocampal region gene expression with the Allen Brain Atlas. This experiment allowed us to classify the region-specific gene expression of the mouse hippocampal region and provided visualization of clustering results and a brain atlas in an integrated manner. This study has the potential to allow neuroscientists to search for experimental groups of genes more quickly and design an effective test according to the new form of data. It is also expected that it will enable the discovery of a more specific sub-region beyond the current known anatomical regions of the brain.

• Proceedings of the KAIS Fall Conference
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• 한국산학기술학회 2003년도 Proceeding
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.

• The Journal of Information Systems
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• 제30권4호
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• pp.329-347
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• 2021

Purpose The purpose of this study is to examine the review of previous studies based on audiobooks, 1) using the IPA method, we examine the quality of audiobooks that listeners perceive as important and the quality of preferred audiobooks, 2) improvement directions for activating audio content and audiobook platform, to seek ways to establish strategic plans for the audiobook market. Design/methodology/approach This study conducted and experiment using three factors(usability, information and interaction) used in web service quality(WebQual). Usability consists of the availability of time, such as the use of audiobooks for hobbies and the ease of search. Information consists of the trends of the times, topics of converation and the latest trends. Interaction consists of the proportion and depth of celebrities. After that, grid analysis was performed using the traditional IPA method and the revised IPA method. It was judged that identifying and analyzing the preference for service provision and new content was more valuable than the Performance used in the existing method. Findings According to the results of the empirical analysis, it was found that the conversation material corresponding to informativity did not affect the audiobook during the online quality analysis. It is believed that improvements can be found through content production related to entertainment, webtoons, and general common sense that can increase listeners' interest. In addition, it was found that the advertising effect of celebrities corresponding to interactivity did not affect the results of this study, which should increase the concentration and immersion of listeners by increasing the technology for audiobooks.

• Geophysics and Geophysical Exploration
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• 제24권4호
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• pp.164-170
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• 2021

Frequency-domain and time-domain induced polarization methods can provide spectral information about subsurface media. Analysis of spectral characteristics has been studied mainly in the frequency-domain, however, time-domain induced polarization research has recently become popular. In this study, assuming a homogeneous half-space model, an inversion method was developed to extract Cole-Cole parameters from the measured secondary potential or electrical resistivity. Since the Cole-Cole parameters of chargeability, time constant, and frequency index are not independent of each other, various problems, such as slow convergence rate, initial model problem, local minimum problem, and divergence, frequently occur when conventional nonlinear inversion is applied. In this study, we developed an effective inversion method using the initial model close to the true model by introducing a grid search method. Finally, the validity of the developed inversion method was verified using inversion experiments.

• Geophysics and Geophysical Exploration
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• 제24권4호
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• pp.171-179
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• 2021

We outline a process for estimating Cole-Cole parameters from time-domain induced polarization (IP) data. The IP transients are all inverted to 2D Cole-Cole earth models that include resistivity, chargeability, relaxation time, and the frequency exponent. Our inversion algorithm consists of two stages. We first convert the measured voltage decay curves into time series of current-on time apparent resistivity to circumvent the negative chargeability problem. As a first step, a 4D inversion recovers the resistivity model at each time channel that increases monotonically with time. The desired intrinsic Cole-Cole parameters are then recovered by inverting the resistivity time series of each inversion block. In the second step, the Cole-Cole parameters can be estimated readily by setting the initial model close to the true value through a grid search method. Finally, through inversion procedures applied to synthetic data sets, we demonstrate that our algorithm can image the Cole-Cole earth models effectively.

• Journal of Korea Water Resources Association
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• 제55권spc1호
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• pp.1197-1210
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• 2022

Recently, users' complaints on drinking water quality are increasing according to emerging interest in the drinking water service issues such as pipe aging and various water quality accidents. In the case of drinking water quality complaints, not only the water pollution but also the inconvenience on the chlorine residual for disinfection are included, thus various efforts, such as rechlorination treatment, are being attempted in order to keep the chlorine concentration supplied evenly. In this research, for a more accurate water quality simulation of water distribution network, the water quality reaction coefficients were estimated, and an optimization method of chlorination/ rechlorination scheduling was proposed consideirng satisfaction of water quality standards and chlorine residual equalization. The proposed method was applied to a large-scale real water network, and various chlorination schemes were comparatively analyzed through the grid search algorithm and optimized based on the suitability and uniformity of supplied chlorine residual concentration.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• The Journal of the Convergence on Culture Technology
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• 제9권5호
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• pp.569-582
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• 2023

Lead-acid Battery is the oldest rechargeable battery system and has maintained its position in the rechargeable battery field. The battery causes thermal runaway for various reasons, which can lead to major accidents. Therefore, preventing thermal runaway is a key part of the battery management system. Recently, research is underway to categorize thermal runaway battery cells into machine learning. In this paper, we present a thermal runaway hazard cell detection and verification algorithm using DBSCAN and statistical method. An experiment was conducted to classify thermal runaway hazard cells using only the resistance values as measured by the Battery Management System (BMS). The results demonstrated the efficacy of the proposed algorithms in accurately classifying thermal runaway cells. Furthermore, the proposed algorithm was able to classify thermal runaway cells between thermal runaway hazard cells and cells containing noise. Additionally, the thermal runaway hazard cells were early detected through the optimization of DBSCAN parameters using a grid search approach.