• 로봇학회논문지
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• 제19권2호
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• pp.176-187
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• 2024

Recently, automated solutions using collaborative robots have been emerging in various industries. Their primary functions include Pick & Place, Peg in the Hole, fastening and assembly, welding, and more, which are being utilized and researched in various fields. The application of these robots varies depending on the characteristics of the grippers attached to the end of the collaborative robots. To grasp a variety of objects, a gripper with a high degree of freedom is required. In this paper, we propose a service automation system using a multi-degree-of-freedom gripper, collaborative robots, and vision sensors. Assuming various products are placed at a checkout counter, we use three cameras to recognize the objects, estimate their pose, and create grasping points for grasping. The grasping points are grasped by the multi-degree-of-freedom gripper, and experiments are conducted to recognize barcodes, a key task in service automation. To recognize objects, we used a CNN (Convolutional Neural Network) based algorithm and point cloud to estimate the object's 6D pose. Using the recognized object's 6d pose information, we create grasping points for the multi-degree-of-freedom gripper and perform re-grasping in a direction that facilitates barcode scanning. The experiment was conducted with four selected objects, progressing through identification, 6D pose estimation, and grasping, recording the success and failure of barcode recognition to prove the effectiveness of the proposed system.

• 농업과학연구
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• 제50권4호
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• pp.675-696
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• 2023

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R²) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.

• 한국농공학회논문집
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• 제66권4호
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• pp.27-39
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• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• 한국인터넷방송통신학회논문지
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• 제24권1호
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• pp.17-22
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• 2024

본 논문은 딥러닝을 기반으로 한 가상 피팅 기능을 갖춘 중고 의류 거래 시스템의 구현을 소개한다. 제안된 시스템은 사용자가 중고 의류를 온라인으로 시각적으로 착용하고 핏을 확인할 수 있는 기능을 제공한다. 이를 위해, 합성곱(CNN) 알고리즘을 사용하여 사용자의 신체 형상과 의류의 디자인을 고려한 가상 착용 모습을 생성한다. 이를 통해 구매자는 온라인에서 실제로 의류를 입기 전에 핏을 미리 확인할 수 있으며, 이는 구매 결정에 도움을 준다. 또한, 판매자는 시스템을 통해 정확한 의류 사이즈와 핏을 제시할 수 있어 구매자의 만족도를 높일 수 있다. 본 논문은 CNN 모델의 학습 절차, 시스템의 구현 방법, 사용자 피드백 등을 자세히 다루고, 실험 결과를 통해 제안된 시스템의 유효성을 입증한다.

• 대한치매학회지
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• 제22권2호
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• pp.61-68
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• 2023

Background and Purpose: Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer's patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images. Methods: A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores. Results: The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03). Conclusions: Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.

• Korean Journal of Radiology
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• 제21권7호
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• pp.891-899
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• 2020

Objective: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. Materials and Methods: We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455). Results: All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement. Conclusion: We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• 융합보안논문지
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• 제23권4호
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• pp.61-70
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• 2023

클러스터링은 데이터의 정답값(실제값)이 없는 데이터를 기반으로 데이터의 특징벡터의 거리 기반 등으로 군집화를 하는 비지도학습 방법이다. 이 방법은 이미지, 텍스트, 음성 등 다양한 데이터에 대해서 라벨링이 없이 적용할 수 있다는 장점이 있다. 기존 클러스터링을 하기 위해 차원축소 기법을 적용하거나 특정 특징만을 추출하여 군집화하는 방법이 적용되었다. 하지만 딥러닝 기반 모델이 발전하면서 입력 데이터를 잠재 벡터로 표현하는 오토인코더, 생성 적대적 네트워크 등을 통해서 딥 클러스터링의 기술이 연구가 되고 있다. 본 연구에서, 딥러닝 기반의 딥 클러스터링 기법을 제안하였다. 이 방법에서 오토인코더를 이용하여 입력 데이터를 잠재 벡터로 변환하고 이 잠재 벡터를 클러스터 구조에 맞게 벡터 공간을 구성 및 k-평균 클러스터링을 하였다. 실험 환경으로 pytorch 머신러닝 라이브러리를 이용하여 데이터셋으로 MNIST와 Fashion-MNIST을 적용하였다. 모델로는 컨볼루션 신경망 기반인 오토인코더 모델을 사용하였다. 실험결과로 k가 10일 때, MNIST에 대해서 89.42% 정확도를 가졌으며 Fashion-MNIST에 대해서 56.64% 정확도를 가진다.

• 박물관보존과학
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• 제31권
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• pp.55-70
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• 2024

스타일 전이 알고리즘은 현재 활발히 연구되는 분야로 일반 이미지를 고전 회화 스타일로 전이시키는 알고리즘도 개발되었다. 그러나 우리나라의 문화유산 이미지에 적용하였을 때 적절한 성과를 보이지 않으며, 적용 사례도 부족한 실정이다. 이에 본 연구에서는 우리나라 문화유산 스타일로 응용할 수 있는 스타일 전이 알고리즘을 개발하고자 한다. 이는 표현 학습을 통해 유의미한 특성을 학습하여 데이터에 대한 이해도를 높였으며, 대상 이미지 내에서 배경과 문화유산을 분리하고, 스타일 이미지에서 원하는 색상과 질감의 스타일 영역을 추출할 수 있게 제작하였다. 이를 통해 대상 이미지의 형태를 유지하면서 스타일 이미지의 특징을 효과적으로 전이하여 새로운 이미지를 생성할 수 있으며, 다양한 문화유산 스타일을 전이시킬 수 있음을 확인하였다.

• 한국간담췌외과학회지
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• 제28권1호
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• pp.14-24
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• 2024

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.