• Journal of the Korean GEO-environmental Society
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• 제20권3호
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• pp.15-20
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• 2019

A large amount of site investigation data is essential to obtain reliable design value. However, site investigations are generally insufficient due to economic problems. It is important to estimate the ground profile information in unboring region for accurate earthwork-volume prediction, and such ground profile information can be estimated by using the geo-statistical approach. Furthermore, the ground profile information in unboring region can be estimated by training a model via machine learning technique such as artificial neural network. In this paper, artificial neural network-based model estimated the ground profile information in unboring region, and this results were compared with that of ordinary kriging technique, which is referred to the geo-statistical approach. Accordingly, a total of 84 ground profile information in an actual bridge environment was split into 75 training and 9 test databases. The observed ground profile information of the test database was compared with those of the ordinary kriging technique and artificial neural network.

• Journal of Digital Convergence
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• 제17권5호
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• pp.217-223
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• 2019

A study in predicting defects of spot welding in real time in automotive field is essential for cost reduction and high quality production. Welding quality is determined by shear strength and the size of the nugget, and results depend on different independent variables. In order to develop the real-time prediction system, multiple regression analyses were conducted and the two dependent variables were obtained with sufficient statistical results with three independent variables, however, the quality prediction by the regression formula could not ensure accuracy. In this study, a multi-layer neural network circuit was constructed. The neural network by 10 dynamic resistance variables was constructed with three hidden layers to obtain execution functions and weighting matrix. In this case, the neural network was established with three independent variables based on regression analysis, as there could be difficulties in real-time control due to too many input variables. As a result, all test data were divided into poor, partial, and modalities. Therefore, a real-time welding quality determination system by three independent variables obtained by multiple regression analysis was completed.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제19권12호
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• pp.7-13
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• 2018

The strength of concrete is affected significantly not only by the internal influence factors of cement, water, sand, aggregate, and admixture, but also by the external influence factors of concrete placement delay and curing temperature. The objective of this research was to predict the concrete strength considering both the internal and external influence factors when concrete is placed at the construction site. In this study, a concrete strength test was conducted on the 24 combinations of internal and external influence factors, and a neural network model was constructed using the test data. This neural network model can predict the concrete strength considering the external influence factors of the concrete placement delay and curing temperature when concrete is placed at the construction site. Contractors can use the concrete strength prediction neural network model to make concrete more robust to external influence factors during concrete placement at a construction site.

• Journal of the Society of Disaster Information
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• 제17권2호
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• pp.329-340
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• 2021

Purpose: In this paper, using artificial neural network algorithm, the Korean Peninsula was analyzed for drought vulnerable areas by predicting weather data changes. Method: Monthly cumulative precipitation data were utilized for research areas considering the specific nature areas, and weather data prediction through artificial neural network algorithm was carried out using statistical program R. The predicted data were applied to the Standardized Precipitation Index (SPI) to analyze drought vulnerable areas in the Korean Peninsula. Result: In this paper, the correlation coefficient values between real and predicted data are found to be 0.043879 higher on average than the regression results, using artificial neural network algorithms. Conclusion: The results of the research are expected to be used as basic research materials for responding to drought.

• Korean Journal of Agricultural Science
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• 제47권4호
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• pp.1109-1122
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• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제24권12호
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• pp.1581-1587
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• 2020

With the emergence of convolutional neural network in the field of machine learning, the model for solving image processing problems has seen rapid development. However, the computing resources required are also rising, making it difficult to learn from a typical environment. Attention mechanism is originally proposed to prevent the gradient vanishing problem of the recurrent neural network, but this can also be used in a direction favorable to learning of the convolutional neural network. In this paper, attention mechanism is applied to convolutional neural network, and the excellence of the proposed method is demonstrated through the comparison of learning time and performance difference at this time. The proposed model showed that both learning time and performance were superior in object detection based on YOLO compared to models without attention mechanism, and experimentally demonstrated that learning time could be significantly reduced. In addition, this is expected to increase accessibility to machine learning by end users.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제24권3호
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• pp.349-354
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• 2020

The preliminary investigation to expand the green space requires a lot of cost and time. In this paper, we solve the problem by measuring the ratio of green space in a specific region through a convolutional neural network based the green space classification using Google Earth images. First, the proposed method collects various region images in Google Earth and learns them by using the convolutional neural network. The proposed method divides the image recursively to measure the green space ratio of the specific region, and it determines whether the divided image is green space using a trained convolutional neural network model, and then the green space ratio is calculated using the regions determined as the green space. Experimental results show that the proposed method shows high performance in measuring green space ratios in various regions.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제24권4호
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• pp.501-507
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• 2020

This paper proposes a new frame synchronization technique based on convolutional neural network (CNN). The conventional frame synchronizers usually find the matching instance through correlation between the received signal and the preamble. The proposed method converts the 1-dimensional correlator ouput into a 2-dimensional matrix. The 2-dimensional matrix is input to a convolutional neural network, and the convolutional neural network finds the frame arrival time. Specifically, in additive white gaussian noise (AWGN) environments, the received signals are generated with random arrival times and they are used for training data of the CNN. Through computer simulation, the false detection probabilities in various signal-to-noise ratios are investigated and compared between the proposed CNN-based technique and the conventional one. According to the results, the proposed technique shows 2dB better performance than the conventional method.

• Journal of Broadcast Engineering
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• 제27권3호
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• pp.332-340
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• 2022

Recently, with the development of deep learning and artificial neural network technologies, research on the application of neural network has been actively conducted in the field of video coding. In particular, deep learning-based intra prediction is being studied as a way to overcome the performance limitations of the existing intra prediction techniques. This paper presents a method of context-adaptive neural network-based intra prediction model training and its coding performance analysis. In other words, in this paper, we implement and train a known intra prediction model based on convolutional neural network (CNN) that predicts a current block using contextual information from reference blocks. Then, we integrate the trained model into HM16.19 as an additional intra prediction mode and evaluate the coding performance of the trained model. Experimental results show that the trained model gives 0.28% BD-rate bit saving over HEVC in All Intra (AI) coding mode. In addition, the coding performance change of training considering block partition is also presented.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• 제35권2호
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• pp.134-142
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• 2022

As industry and technology go through advancement, it is hard to search new materials which satisfy various standards through conventional trial-and-error based research methods. Crystal Graph Convolutional Neural Network(CGCNN) is a neural network which uses material's features as train data, and predicts the material properties(formation energy, bandgap, etc.) much faster than first-principles calculation. This report introduces how to train the CGCNN model which predicts the formation energy using open database. It is anticipated that with a simple programming skill, readers could construct a model using their data and purpose. Developing machine learning model for materials science is going to help researchers who should explore large chemical and structural space to discover materials efficiently.