• Journal of the Korean Crystal Growth and Crystal Technology
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• v.31 no.6
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• pp.258-263
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• 2021

ZnO varistor is a semiconductor device which can serve to protect the circuit from surge voltage because its non-linear I-V characteristics by controlling the microstructure of grain and grain boundaries. In order to obtain desired electrical properties, it is important to control microstructure evolution during the sintering process. In this research, we defined a dataset composed of process conditions of sintering and relative permittivity of sintered body, and collected experimental dataset with DOE. Meta-models can predict permittivity were developed by learning the collected experimental dataset on various machine learning algorithms. By utilizing the meta-model, we can derive optimized sintering conditions that could show the maximum permittivity from the numerical-based HMA (Hybrid Metaheuristic Algorithm) optimization algorithm. It is possible to search the optimal process conditions with minimum number of experiments if meta-model-based optimization is applied to ceramic processing.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.179-184
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• 2022

In this paper, we are going to study how we can automatically determine the number of convolutional filters for the optimal model without a search algorithm. This paper proposes HSE Block by connecting SE Block proposed in SENet to a convolutional neural network and connecting a convolutional neural network not learned at the bottom. An experiment was conducted to increase the number of filters by one per 3 epoch using two datasets for the HSEBlock model and to increase the number of filters by the value in the filter. Based on this experiment, the model was constructed with multi-layer HSE Block instead of layer HSE Block, and the experiment was carried out using a dataset that was more difficult to learn than the one used in the previous experiment. The effect of HSE Block was verified by conducting an experiment with the number of HSE Blocks set to 2, 3, 4, and 5 on a dataset that is more difficult to learn than before.

• Korean Journal of Construction Engineering and Management
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• v.17 no.1
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• pp.3-9
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• 2016

This study proposes a approximate cost estimation system based on Excel with VBA using weighted CBR(Case Based Reasoning). One characteristic of this system is that it generates the sheet automatically as many as the number of similar case and new estimation when it performs a case learning and a new estimate and cell formula is automatically entered into each sheet. User can be free to compose a combination of attribute factors because they can select up to ten attribute factors. This paper presents an applicability of estimation model for estimating the soil remediation cost when it use a landfarming method. When compared to a estimation model by using average unit cost and optimum multiple regression, this model shows a better result. This study was aimed at landfarming method, but it is expected that a cost estimation model using CBR will be more likely to apply in soil remediation technologies which various remediation technologies and pollutant species exist.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.82-93
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• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).

• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.47-63
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• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.

• The Journal of Engineering Geology
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• v.15 no.1
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• pp.67-76
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• 2005

This paper presents the application of a neural network for prediction of the unconfined compressive strength from physical properties and schmidt hardness number on rock samples. To investigate the suitability of this approach, the results of analysis using a neural network are compared to predictions obtained by statistical relations. The data sets containing 55 rock sample records which are composed of sandstone and shale were assembled in Daegu area. They were used to learn the neural network model with the back-propagation teaming algorithm. The rock characteristics as the teaming input of the neural network are: schmidt hardness number, specific gravity, absorption, porosity, p-wave velocity and S-wave velocity, while the corresponding unconfined compressive strength value functions as the teaming output of the neural network. A data set containing 45 test results was used to train the networks with the back-propagation teaming algorithm. Another data set of 10 test results was used to validate the generalization and prediction capabilities of the neural network.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.885-896
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• 2022

Motor failure in manufacturing plays an important role in future A/S and reliability. Motor failure is detected by measuring sound, current, and vibration. For the data used in this paper, the sound of the car's side mirror motor gear box was used. Motor sound consists of three classes. Sound data is input to the network model through a conversion process through MelSpectrogram. In this paper, various methods were applied, such as data augmentation to improve the performance of classifying fault motors and various methods according to class imbalance were applied resampling, reweighting adjustment, change of loss function and representation learning and classification into two stages. In addition, the curriculum learning method and self-space learning method were compared through a total of five network models such as Bidirectional LSTM Attention, Convolutional Recurrent Neural Network, Multi-Head Attention, Bidirectional Temporal Convolution Network, and Convolution Neural Network, and the optimal configuration was found for motor sound classification.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.259-264
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• 2023

In this study, we devised a parametric analysis workflow for efficiently analyzing the material properties of 3D woven materials. The parametric model uses wire spacing in the woven materials as a design parameter; we generated 2,500 numerical models with various combinations of these design parameters. Using MATLAB and ANSYS software, we obtained various material properties, such as bulk modulus, thermal conductivity, and fluid permeability of the woven materials, through a parametric batch analysis. We then used this large dataset of material properties to perform a regression analysis to validate the relationship between design variables and material properties, as well as the accuracy of numerical analysis. Furthermore, we constructed an artificial neural network capable of predicting the material properties of 3D woven materials on the basis of the obtained material database. The trained network can accurately estimate the material properties of the woven materials with arbitrary design parameters, without the need for numerical analyses.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.793-810
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• 2022

Synthetic Aperture Radar (SAR) is considered to be suitable for near real-time inundation monitoring. The distinctly different intensity between water and land makes it adequate for waterbody detection, but the intrinsic speckle noise and variable intensity of SAR images decrease the accuracy of waterbody detection. In this study, we suggest two modules, named 'morphology module' and 'edge-enhanced module', which are the combinations of pooling layers and convolutional layers, improving the accuracy of waterbody detection. The morphology module is composed of min-pooling layers and max-pooling layers, which shows the effect of morphological transformation. The edge-enhanced module is composed of convolution layers, which has the fixed weights of the traditional edge detection algorithm. After comparing the accuracy of various versions of each module for U-Net, we found that the optimal combination is the case that the morphology module of min-pooling and successive layers of min-pooling and max-pooling, and the edge-enhanced module of Scharr filter were the inputs of conv9. This morphologic and edge-enhanced U-Net improved the F1-score by 9.81% than the original U-Net. Qualitative inspection showed that our model has capability of detecting small-sized waterbody and detailed edge of water, which are the distinct advancement of the model presented in this research, compared to the original U-Net.

• Journal of the Korean Geosynthetics Society
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• v.23 no.1
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• pp.17-25
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• 2024

The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.