• 디지털산업정보학회논문지
• /
• 제14권4호
• /
• pp.69-77
• /
• 2018

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

• 한국의학물리학회지:의학물리
• /
• 제30권2호
• /
• pp.49-58
• /
• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

• 정보교육학회논문지
• /
• 제6권1호
• /
• pp.1-12
• /
• 2002

본 논문은 초등학생의 수학과 학습능력을 진단하여서 결과에 따라 개별학습을 할 수 있는 LED 시스템 (LEarner Diagnosis system)을 설계 구현한 것이다. 수학과와 같이 위계적으로 학습이 이루어지는 교과는 선수학습을 제대로 성취하지 못하면 후속학습에 곤란을 겪는다. 이를 방지하기 위해 LED 시스템은 학습 전에 학습자의 선수 학습 능력을 진단한다. 진단 결과 학습 결손이 있을 때는 보충학습을 통해 결손을 보충하고 본 주제학습을 한다. 또한 매 학습이 완전학습이 되어 더 이상의 학습결손이 발생하지 않도록 학습 후에도 진단평가를 실시하여 완전학습이 이루어졌을때만 후속학습을 할 수 있도록 한다. LED 시스템을 이용하면 학습자는 학습결손을 방지할 수 있고 완전학습을 하기 때문에 궁극적으로 학습 목표에 도달 할 수 있게 된다.

• 유체기계공업학회:학술대회논문집
• /
• 유체기계공업학회 1999년도 유체기계 연구개발 발표회 논문집
• /
• pp.317-323
• /
• 1999

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal vibration diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised teaming algorithm is used to improve the quality of the classifier decision regions.

• Journal of Computing Science and Engineering
• /
• 제10권4호
• /
• pp.118-127
• /
• 2016

An improved sample balanced genetic algorithm and Extreme Learning Machine (iSBGA-ELM) was designed for accurate diagnosis of Alzheimer disease (AD) and identification of biomarkers associated with AD in this paper. The proposed AD diagnosis approach uses a set of magnetic resonance imaging scans in Open Access Series of Imaging Studies (OASIS) public database to build an efficient AD classifier. The approach contains two steps: "voxels selection" based on an iSBGA and "AD classification" based on the ELM. In the first step, the proposed iSBGA searches for a robust subset of voxels with promising properties for further AD diagnosis. The robust subset of voxels chosen by iSBGA is then used to build an AD classifier based on the ELM. A robust subset of voxels keeps a high generalization performance of AD classification in various scenarios and highlights the importance of the chosen voxels for AD research. The AD classifier with maximum classification accuracy is created using an optimal subset of robust voxels. It represents the final AD diagnosis approach. Experiments with the proposed iSBGA-ELM using OASIS data set showed an average testing accuracy of 87%. Experiments clearly indicated the proposed iSBGA-ELM was efficient for AD diagnosis. It showed improvements over existing techniques.

• 소음진동
• /
• 제10권2호
• /
• pp.331-337
• /
• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• 동의생리병리학회지
• /
• 제23권5호
• /
• pp.942-949
• /
• 2009

We in this paper propose the method for diagnosis patients through the reasoning based on the diagnosis ontology in oriental medicine. In prior studies, it is simply diagnosed with the information of main symptoms, optional symptoms, and tongue / pulse. In addition, ontology itself has subjective opinions of oriental medical doctors for patients in form of axioms. There is a problem in latter case that it is difficult for other oriental medical doctors to change knowledge within the ontology. In order to solve these problems, we have constructed the diagnosis ontology and the reasoning algorithm as followings: First, in order to raise the diagnosis accuracy, we constructed the diagnosis ontology with pattern identifications, main symptoms, optional symptoms, and tongue / pulse. We also utilize the diagnosis points described in the pathology textbook, which has been studied in all of domestic oriental medical colleges. This information is represented as OWL instances in ontology, not OWL axioms so that it can be easily updated. Second, we suggest the algorithms for diagnosis reasoning and learning method based on the ontology. We have implemented the reasoning and learning system according to the diagnosis algorithm. In future study, we will construct the diagnosis ontology with all of pattern identifications and symptoms within the pathology textbook.

• Nuclear Engineering and Technology
• /
• 제55권2호
• /
• pp.493-505
• /
• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• 한국음향학회지
• /
• 제39권4호
• /
• pp.263-269
• /
• 2020

본 논문에서는 정상상태와 8가지의 고장이 재현가능한 회전체를 제작하고 진동 데이터를 취득하였다. 취득한 데이터로 특징을 계산하여 인공신경망과 유전알고리즘을 적용한 고장진단을 통해 정확성을 분석한다. 최적의 시간과 높은 정확성의 구현을 위해 특징을 3가지 영역으로 구분하여 고장진단에 적용하였다. 설정변수는 학습수로 설정하였다. 회전체 고장진단의 결과는 다른 영역보다 주파수영역에서 높은 정확성을 보였으며 학습수 5000, 8000회에서 10회의 구동 모두 정확한 고장진단을 하였다. 시간의 효율성을 고려하였을 경우, 학습수가 5000회일 때 가장 우수하다고 판단하였다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제11권4호
• /
• pp.179-188
• /
• 2022

기계의 주요 부품인 베어링 결함 진단에 딥러닝을 활용하는 연구가 활발하게 진행되어 좋은 성능을 달성하였으나, 학습 데이터와 테스트 데이터의 운영 환경 차이로 인해 기계가 실제로 가동되는 환경에서는 성능 저하가 발생하는 문제가 있다. 학습 데이터와 테스트 데이터의 분포 차이 문제를 다루는 방법으로 데이터 적응이 제안되어 좋은 결과를 보여주고 있으나, 각 방법이 가정하고 있는 특정 적용 시나리오를 벗어나기 어렵다는 제약이 있다. 이에 본 연구는 MFCCs를 이용한 입력 데이터의 변환과 간단한 CNN 구조를 이용해 원시 도메인 데이터로부터 생성된 모델에 대해 추가적인 학습이나 조정 없이 타겟 도메인 데이터에 대한 테스트를 강건하게 수행하는 방법을 제안하였으며, 대표적인 베어링 결함 진단 데이터셋인 CWRU 베어링 데이터를 이용해 제안한 방법에 대한 실험 및 분석을 수행하였다. 실험 결과 전이 학습 기반의 방법들과 대등한 성능을 보였으며, 입력 변환 기반의 베이스라인 방법보다는 최소 15% 정도의 높은 성능을 달성하였다.