• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.

• 응용통계연구
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• 제28권5호
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• pp.921-932
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• 2015

본 연구에서는 증강된 보병대대의 과학화 전투훈련 데이터 중 공격작전에 관한 장병들의 생존분석을 실시하였다. 과학화 전투훈련은 KCTC(Korea Combat Training Center)로 불리는 전투훈련장에서 MILES(Multiple Integrated Lazer Engagement System)와 중앙통제장비체계 등 과학화된 훈련장비와 체계 운용하 훈련부대가 적 전술 및 무기체계를 사용하는 전문 대항군과 실시하는 쌍방 자유기동훈련이다. 이는 훈련기간 동안 훈련지역의 모든 데이터가 저장되어 훈련통제 뿐 아니라 분석 및 사후검토를 할 수 있는 첨단화된 군사 훈련으로 통계적 분석이 가능한 데이터를 제공한다. 분석방법은 모수적 분포 가정이 필요하지 않은 Cox의 비례위험모형을 적용하였으며, 보다 풍부하고 용이한 해석을 위해 의사결정나무모형(CART(Classification and Regression Trees), GUIDE(Generalized, Unbiased, Interaction Detection and Estimation), CTREE(Conditional Inference Trees))을 활용하였다. Cox 비례위험모형의 비례성 가정을 확인하여 이를 위배하는 변수에 대해서 층화하여 분석하고, Cox 비례위험모형 결과 복무기간에 관한 해석이 용이하지 않아 단변량으로 local 회귀분석을 통해 추가적인 해석을 시도하였다. CART, GUIDE, CTREE는 모형의 특성별로 나무모형을 형성하며 이를 통하여 다양한 해석이 가능하다.

• 로봇학회논문지
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• 제18권3호
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• pp.293-298
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• 2023

The military is facing a continuous decrease in personnel, and in order to cope with potential accidents and challenges in operations, efforts are being made to reduce the direct involvement of personnel by utilizing the latest technologies. Recently, the use of various sensors related to Manned-Unmanned Teaming and artificial intelligence technologies has gained attention, emphasizing the need for flexible utilization methods. In this paper, we propose four dataset construction methods that can be used for effective training of robots that can be deployed in military operations, utilizing not only RGB image data but also data acquired from IR image sensors. Since there is no publicly available dataset that combines RGB and IR image data, we directly acquired the dataset within buildings. The input values were constructed by combining RGB and IR image sensor data, taking into account the field of view, resolution, and channel values of both sensors. We compared the proposed method with conventional RGB image data classification training using the same learning model. By employing the proposed image data fusion method, we observed improved stability in training loss and approximately 3% higher accuracy.

• Journal of Information Processing Systems
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• 제8권4호
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• pp.575-588
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• 2012

Exploiting unlabeled text data with a relatively small labeled corpus has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. Biomedical named-entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. This paper proposes an Active Co-Training (ACT) algorithm for biomedical named-entity recognition. ACT is a semi-supervised learning method in which two classifiers based on two different feature sets iteratively learn from informative examples that have been queried from the unlabeled data. We design a new classification problem to measure the informativeness of an example in unlabeled data. In this classification problem, the examples are classified based on a joint view of a feature set to be informative/non-informative to both classifiers. To form the training data for the classification problem, we adopt a query-by-committee method. Therefore, in the ACT, both classifiers are considered to be one committee, which is used on the labeled data to give the informativeness label to each example. The ACT method outperforms the traditional co-training algorithm in terms of f-measure as well as the number of training iterations performed to build a good classification model. The proposed method tends to efficiently exploit a large amount of unlabeled data by selecting a small number of examples having not only useful information but also a comprehensive pattern.

• Journal of Platform Technology
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• 제8권1호
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• pp.16-23
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• 2020

상표권 보호를 위한 상표 이미지의 진위 결정에 심층 신경망인 합성곱 신경망을 이용할 수 있다. 이를 위해, 상표로 등록되어 있는 한 장의 상표 이미지를 반복적으로 학습하는 것은 기계학습의 성능을 감소시키는 원인이 된다. 따라서, 이러한 응용에서 학습 데이터는 다양한 방법으로 생성된다. 하지만 대상 이미지가 회전되어 있으면 원본이라 하더라도 인식하지 못하거나 위조 상표로 분류되기도 한다. 본 논문에서는 회전한 상표 이미지의 진위 결정을 위한 기계학습 데이터의 확장 방법을 제안한다. 본 논문에서 제안하는 학습 데이터 확장 방법은 기울어진 이미지를 생성하고 이를 학습 데이터로 사용하는 것이다. 본 논문에서 제안하는 학습 데이터 확장 방법의 유효성 검증을 위해 대학의 로고를 대상으로 학습 데이터를 생성하였으며, 이를 활용하여 합성곱 신경망을 학습시킨 후 검증용 데이터를 이용하여 정확도를 평가하였다. 정확도 평가 결과에 따르면 본 논문에서 제안한 방법으로 생성한 학습 데이터를 활용하면 회전한 상표를 대상으로 한 진위 여부 결정에 합성곱 신경망을 활용할 수 있다.

• 한국터널지하공간학회 논문집
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• 제21권3호
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• pp.419-432
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• 2019

대부분 딥러닝 모델의 학습은 입력값과 입력값에 따른 출력값이 포함된 레이블링 데이터(labeling data)를 학습하는 지도 학습(supervised learning)으로 진행된다. 레이블링 데이터는 인간이 직접 제작하므로 데이터의 정확도가 높다는 장점이 있지만 비용과 시간의 문제로 인해 데이터의 확보에 많은 노력이 소요된다. 그리고 지도 학습의 목표는 정탐지 데이터(true positive data)의 인식 성능 향상에 초점이 맞추어져 있으며, 오탐지 데이터(false positive data)의 발생에 대한 대처는 미흡한 실정이다. 본 논문은 터널 관제센터에 투입된 딥러닝 모델 기반 영상유고 시스템의 모니터링을 통해 정탐지와 레이블링 데이터의 학습으로 예측하기 힘든 오탐지의 발생을 확인하였다. 오탐지의 유형은 작업차량의 경광등, 터널 입구부에서 반사되는 햇빛, 차선과 차량의 일부에서 발생하는 길쭉한 검은 음영 등이 화재와 보행자로 오탐지되고 있었다. 이러한 문제를 해결하기 위해 현장에서 발생한 오탐지 데이터와 레이블링 데이터를 동시에 학습하여 딥러닝 모델을 개발하였으며, 그 결과 기존 레이블링 데이터만 학습한 모델과 비교하면 레이블링 데이터에 대한 재추론 성능이 향상됨을 알 수 있었다. 그리고 오탐지 데이터에 대한 재추론을 한 결과 오탐지 데이터를 많이 포함하여 학습한 모델일 경우 보행자의 오탐지 개수가 훨씬 줄었으며, 오탐지 데이터의 학습을 통해 딥러닝 모델의 현장 적용성을 향상시킬 수 있었다.

• 대한간호학회지
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• 제29권6호
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• pp.1244-1253
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• 1999

The purpose of this study was to explore and describe the experience of Dan-Jeon breathing training and of Qi as a essential substance in forming human body. The sample consists of 7 participants who are Dan-Jeon Breathing training in a Training center, Pusan, Korea. They were asked open-ended questions in order for them to talk about their experiences. With permission of the subjects, the interviews were recorded and transcribed. The summarized results of this research are following. 1. The purpose of Dan-Jeon Breathing The interview data was organized by themes into 4 categories : hope for health recovery, a concern about Dan-Jeon Breathing, seeking meaning of life, change of lifestyle 2. The experience of Qi during Dan-Jeon Breathing training The interview data was organized by themes into 3 categories : an autonomic movement of body, spiritual experience, conviction of existence of Qi. 3. The change after Dan-Jeon Breathing training. The interview data was organized by themes into 7 categories : physical health promotion, emotional relaxation, promoting brain function, positive attitude about life, love to others, investigation for self, improvement on Qi feeling..

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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• pp.173-176
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• 2001

This paper proposes training algorithms of neuro-fuzzy systems. First, we introduce a structure training algorithm, which produces the necessary number of hidden nodes from training data. From this algorithm, initial fuzzy rules are also obtained. Second, the parameter training algorithm using evolution strategy is introduced. In order to show their usefulness, we apply our neuro-fuzzy system to a nonlinear system identification problem. It was found from experiments that proposed training algorithms works well.

• 한국소음진동공학회논문집
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• 제12권10호
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• pp.799-807
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• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• 공학교육연구
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• 제19권2호
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• pp.97-101
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• 2016

This paper examines the industry field training education model, analyze the operational status proposed improvement measures. Data were analyzed using a field training participating students participating industry last three years. On the other hand analysis field training participating students increased, industry participation has decreased. And most of the students took part in the seasonal short-term job training. In addition, it was difficult to analyze the employment status field training operations follow-up member. In this paper, a field training operations support system management models and practical training courses organized field trips how to improve. Field training operations support will be strengthened through the work associated with the company expanding participation model introduced and is expected to increase in the long-term practical training, students participate in field training system improvement. Run the job training Improvement in future research presented in this paper attempts to analyze the students' employment status and results of operations involved.