• Computers and Concrete
• /
• 제32권3호
• /
• pp.327-337
• /
• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• 융합정보논문지
• /
• 제12권3호
• /
• pp.17-24
• /
• 2022

모바일 악성 앱이 급증하고 있으며, 전 세계 모바일 OS 시장의 대부분을 차지하고 있는 안드로이드가 모바일 사이버 보안 위협의 주요 대상이 되고 있다. 따라서 빠르게 진화하는 악성 앱에 대응하기 위해 인공지능 구현기술 중 하나인 기계학습을 활용한 악성 앱 탐지 기법의 필요성이 대두되고 있다. 본 논문은 악성 앱의 탐지성능을 향상할 수 있는 특성 선택 및 특성 추출을 이용한 특성 선별 방법을 제안하였다. 특성 선별 과정에서 특성 개수에 따라 탐지 성능이 향상되었으며, 권한보다 API가 상대적으로 좋은 탐지 성능을 보였고, 두 특성을 조합하면 평균 93% 이상의 높은 탐지 정밀도를 보여 적절한 특성의 조합이 탐지 성능을 높일 수 있음을 확인하였다.

• 국제학술발표논문집
• /
• The 9th International Conference on Construction Engineering and Project Management
• /
• pp.304-311
• /
• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• 한국작물학회:학술대회논문집
• /
• 한국작물학회 2022년도 추계학술대회
• /
• pp.88-88
• /
• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• 대한의용생체공학회:의공학회지
• /
• 제45권2호
• /
• pp.90-94
• /
• 2024

Diagnosing wounds presents a significant challenge in clinical settings due to its complexity and the subjective assessments by clinicians. Wound deep learning algorithms quantitatively assess wounds, overcoming these challenges. However, a limitation in existing research is reliance on specific datasets. To address this limitation, we created a comprehensive dataset by combining open dataset with self-produced dataset to enhance clinical applicability. In the annotation process, machine learning based on Gradient Vector Flow (GVF) was utilized to improve objectivity and efficiency over time. Furthermore, the deep learning model was equipped U-net with residual blocks. Significant improvements were observed using the input dataset with images cropped to contain only the wound region of interest (ROI), as opposed to original sized dataset. As a result, the Dice score remarkably increased from 0.80 using the original dataset to 0.89 using the wound ROI crop dataset. This study highlights the need for diverse research using comprehensive datasets. In future study, we aim to further enhance and diversify our dataset to encompass different environments and ethnicities.

• 디지털콘텐츠학회 논문지
• /
• 제18권6호
• /
• pp.1175-1182
• /
• 2017

제4차 산업혁명은 IoT(Internet of Things) 빅데이터(BigData) 인공지능 등 다양한 기술들의 융합을 통하여 스마트 공장(Smart factory) 구현을 목표로 새로운 산업화를 시도하고 있다. 스마트 공장 실현을 위해서 다양한 이종기계들 간의 유연한 데이터 교환 방법이 가능한 통신 기술이 필요하고 표준 기술을 기반 생산 장비의 확장성이 고려될 수 있어야 한다. 본 연구에서는 이종기기를 포함하는 다수의 생산 설비로부터 데이터를 수집 및 통합하고, 모든 생산 장비를 감시할 수 있는 MTConnect기반 이종 공작기계 상태 정보 및 가공 정보 관리시스템을 제안한다. 개발된 시스템 기술은 유연 자동화 생산 라인의 핵심 기술로서 오류 검출, 가공 상태 관리 등 무인 자동화 라인의 중요한 정보를 제공 및 관리하는 기술을 제공할 수 있다.

• 한국농업기계학회:학술대회논문집
• /
• 한국농업기계학회 2017년도 춘계공동학술대회
• /
• pp.82-82
• /
• 2017

현대농업에서 잡초를 조기에 제거하지 못할 경우 작물의 성장에 장애를 초래하므로 제초작업은 매우 중요하다. 최근 친환경 고품질 농산물에 대한 소비자의 관심과 고품질 농산물에 대한 가격 차별화 등으로 제초제를 사용하지 않은 친환경 재배가 주목을 받고 있다. 제초제는 농작물에 따라 분류가 되어있어 종류마다 효과가 다르고 사용방법이 종류에 따라 어떻게 사용되는지 알기가 쉽지 않아 이에 따른 피해 또한 무시 할 수 없다. 현재 상용화되어 있는 제초기는 굴곡이 있는 고랑의 잡초를 효율적으로 제거하기 어려워 밭작물 제초작업에 사용하기 부적합하다. 밭작물 재배 기간에 행해지는 고랑의 제초작업은 평지보다 더 많은 힘이 필요하고 일반적인 제초기로는 좋은 결과를 얻기 어렵다. 또한 제초 작업시 농작물 피해와 멀칭 비닐을 손상시키는 문제가 발생 하고 있다. 따라서 밭작물에 효율적인 잡초를 제거하기 위해 굴곡이 있는 지형의 잡초를 제거하기 위한 제초기 개발이 반드시 필요하다. 밭작물용 제초기를 개발하기 위해 굴곡이 있는 고랑의 잡초 제거가 가능한 제초날의 형상 설계가 우선되어야 하며, 고랑의 잡초제거를 위한 제초날 형상설계를 하기 위해 우리나라 주요 밭작물 재배지의 이랑 넓이, 고랑 깊이 및 고랑 폭을 조사 분석하였다. 조사 작물 대상은 양파, 마늘, 무, 배추, 고추, 당근, 감자 총 7가지 작물을 대상으로 조사하였으며, 각 작물들의 이랑 넓이와 고랑 깊이 및 폭의 평균치를 구하여 제초날 설계에 적용하고자 하였다. 범위가 있는 수치는 높은 수치를 기준으로 계산하였으며 고랑의 평균 폭은 34.2cm이고, 고랑의 평균 깊이는 22.1cm가 나타났다. 잡초를 효과적으로 제거하기 위해 제초날의 형상은 고랑의 크기 및 형태에 맞게 원형으로 설계를 하여야 한다. 형상 설계는 밭작물 고랑 평균직경 및 평균높이를 고려하고, 제초작업시 제초날이 작물 및 멀칭비닐의 손상 하지 않도록 하기 위해 직경은 고랑 평균 폭의 75% 정도 치수로 형상을 설계하고, 제초날 깊이는 고랑의 굴곡형태을 고려할 때 제초날 반경의 70%정도로 설계하여 원형제초날 폭 250mm, 제초날 깊이 87.5mm로 설계해야 할 것으로 분석 되었다.

• 반도체디스플레이기술학회지
• /
• 제6권4호
• /
• pp.23-27
• /
• 2007

Rotary stages in semiconductor, display industry and many other fields require challenging accuracy to perform their functions properly. Especially, Axis of rotation error on rotary system is significant; such as the spindle error motion of the aligner, wire bonder and inspector machine which result in the poor quality products. To evaluate and improve the performance of such precision rotary stage, undesired movements on the other 5 degrees of freedom of the rotary stage must be measured and analyzed. In this paper, we have measured the three translations and two tilt motions of the worm gear type spindle with high precision capacitive sensors. To obtain the radial error motion, we have used Donaldson's reversal technique. And the axial components of the spindle tilt error motion can be obtained accurately from the axial direction outputs of sensors by Estler face motion reversal technique. Further more we have designed and developed the sensor mounting jig with standard cylinder for reversal method.

• 한국정밀공학회지
• /
• 제26권8호
• /
• pp.72-78
• /
• 2009

We developed a line standards measurement system using an optical microscope and measured two kinds of line standards. It consists of three main parts: an optical microscope module including a CCD camera, a stage system with a linear encoder, and a measurement program for a microscopic image processing. The magnification of microscope part was calibrated using one-dimensional gratings and the angular motion of stage was measured to estimate the Abbe error. The threshold level in line width measurement was determined by comparing with certified values of a line width reference specimen, and its validity was proved through the measurement of another line width specimen. The expanded uncertainty (k=2) was about 100 nm in the measurements of $1{\mu}m{\sim}10{\mu}m$ line width. In the comparison results of line spacing measurement, two kinds of values were coincide within the expanded uncertainty, which were obtained by the one-dimensional measuring machine in KRISS and the line standards measurement system. The expanded uncertainty (k=2) in the line spacing measurement was estimated as $\sqrt{(0.098{\mu}m)^2+(1.8{\times}10^{-4}{\times}L)^2}$. Therefore, it will be applied effectively to the calibration of line standards, such as line width and line spacing, with the expanded uncertainty of several hundreds nanometer.

• 한국정밀공학회지
• /
• 제30권5호
• /
• pp.493-498
• /
• 2013

The present study treats the optimization process for a non-linear grinding system with dual time delay, mainly from the energetic viewpoint. To this end, the stability of the grinding system is investigated first with regard to the grinding wheel rotation speed. The concept of grinding energy density is newly proposed as the primary figure of merit and this quantity is evaluated at various stable and limit cycle conditions. The computational results show that simple monotonic trend in energy density is observed under stable conditions, whilst rather complicated behaviors can appear when the conditions are associated with limit cycle oscillations. Finally, the relations between the vibration amplitude and the energy density and their implications on the engineering decision/compromise are discussed.