• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.98-98
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• 2010

그라우팅은 지반의 공학적인 강도증가를 통한 지지력 향상 및 암반의 투수성 저감을 통해 지하수 유동을 억제하기 위하여 대규모 토목공사 현장의 균열암반 및 댐 또는 제방 등의 지역에서 많이 시행되고 있다. 본 연구는 균열암반지역에서 그라우팅 효과 확인을 위하여 보통 포틀랜드 시멘트(OPC)와 마이크로 시멘트(MC)를 사용하여 그라우팅을 수행하였으며, 그라우팅 전후에 물리검층(시추공영상촬영, 초음파 텔레뷰어검층)을 이용하여 그라우팅 효과 정도를 파악하였다. 연구지역은 경상북도 영주시 평은면 지역으로, 지질은 선캠브리아기 안구상편마암에 시대미상의 흑운모 화강암이 관입을 하였고, 이를 제4기의 충적층이 부정합으로 피복되어 있다. 그라우팅은 일반구간과 단층대구간으로 구분하여 실시하였으며, 두 구간의 이격거리는 서로의 간섭을 피하기 위해 약 40m 간격으로 선정하였다. 주입재(OPC, MC)는 5개의 주입공에서 triangle 방향으로 주입하였으며, 주입정도를 확인하기 위하여 각 구간에 2공씩 확인시추를 하였다. 두 개의 site중 일반구간의 보통 포틀랜드시멘트 주입결과 평균주입량은 48.2kg/공이며 주입 1m당으로 환산하면 Lugeon값 10미만의 지층에서는 1.62kg/m이며, 마이크로시멘트의 평균주입량은 49.6kg/공이며 주입 1m당으로 환산하면 Lugeon값 10미만의 지층에서는 3.86kg/m로 나타났다. 단층대 구간에서는 보통 포틀랜드시멘트의 평균주입량이 40.0kg/공이며, 1m당으로 환산하면 Lugeon값 10미만의 지층에서는 2.75kg/m이며, 마이크로 시멘트는 평균주입량이 56.5kg/공, 주입 1m당으로 환산하면 Lugeon값 10미만의 지층에서는 3.15kg/m로 나타났다. 마이크로시멘트의 주입압은 보통 포틀랜드시멘트에 비해 상대적으로 낮았으며, 그라우팅 개선효과 역시 상대적으로 양호한 것으로 나타났다. 그라우팅 효과확인을 위한 물리검층의 초음파텔레뷰어 해석결과 상대암반강도는 주입전 $250{\sim}750\;kgf/cm^2$, 주입후는 $400{\sim}800\;kgf/cm^2$으로 그라우팅에 의한 암반강도의 상승을 확인할 수 있었고, 시추공영상촬영 분석에서는 시추코어만으로 얻기 힘든 시멘트 충진구간을 직접 확인할 수 있었다. 초음파텔레뷰어의 경우 파쇄대의 분포 및 암반강도 측정을 통한 그라우팅 파악은 가능하였으나 파쇄대 충진물을 확인할 수가 없는 단점이 있었고 이를 시추공영상촬영을 통해 보완할 수 있었다. 다만 물리검층의 경우 그라우팅에 의한 공의 손실로 동일공에 의한 반복 조사가 아닌 경우가 대부분이어서 그라우팅 효과에 대한 정확한 비교가 어려웠으며 추후 이를 보완하기 위한 계속적인 연구가 필요할 것으로 사료된다.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.