• Asia pacific journal of information systems
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• 제16권2호
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• pp.85-110
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• 2006

The objective of this research is to identify the process of knowledge transfer and to examine the effect of IT human capability on knowledge transfer in information systems outsourcing. Through a field survey, it is found that clients' IT human capability significantly affects on cooperative learning with vendors and knowledge transfer from vendors to clients. The survey also shows that clients' trust with vendors indirectly enhances the knowledge transfer by increasing the cooperative learning. This study provides a solution of knowledge transfer problem in information systems outsourcing. Also it brings out issues that can be accrued in the outsourcing situation, such as clients' dependency on vendors and knowledge asymmetries developed in favor of the vendors. These issues should be topics for future research on information systems outsourcing.

• Journal of Information Technology Applications and Management
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• 제28권4호
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• pp.33-43
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• 2021

While youth unemployment has recorded the lowest level since the global COVID-19 pandemic, SMEs(small and medium sized enterprises) are still struggling to fill vacancies. It is difficult for SMEs to find good candidates as well as for job seekers to find appropriate job offers due to information mismatch. To overcome information mismatch, this study proposes the fine-turning model for bidirectional HR matching based on a pre-learning language model called BERT(Bidirectional Encoder Representations from Transformers). The proposed model is capable to recommend job openings suitable for the applicant, or applicants appropriate for the job through sufficient pre-learning of terms including technical jargons. The results of the experiment demonstrate the superior performance of our model in terms of precision, recall, and f1-score compared to the existing content-based metric learning model. This study provides insights for developing practical models for job recommendations and offers suggestions for future research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3762-3781
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• 2020

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1293-1308
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• 2020

In fine-tuning-based transfer learning, the size of the dataset may affect learning accuracy. When a dataset scale is small, fine-tuning-based transfer-learning methods use high computing costs, similar to a large-scale dataset. We propose a mid-level feature extractor that retrains only the mid-level convolutional layers, resulting in increased efficiency and reduced computing costs. This mid-level feature extractor is likely to provide an effective alternative in training a small-scale medical image dataset. The performance of the mid-level feature extractor is compared with the performance of low- and high-level feature extractors, as well as the fine-tuning method. First, the mid-level feature extractor takes a shorter time to converge than other methods do. Second, it shows good accuracy in validation loss evaluation. Third, it obtains an area under the ROC curve (AUC) of 0.87 in an untrained test dataset that is very different from the training dataset. Fourth, it extracts more clear feature maps about shape and part of the chest in the X-ray than fine-tuning method.

• 대한의용생체공학회:의공학회지
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• 제39권5호
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• pp.213-219
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• 2018

Stomach cancer is the most diagnosed cancer in Korea. When gastric cancer is detected early, the 5-year survival rate is as high as 90%. Gastroscopy is a very useful method for early diagnosis. But the false negative rate of gastric cancer in the gastroscopy was 4.6~25.8% due to the subjective judgment of the physician. Recently, the image classification performance of the image recognition field has been advanced by the convolutional neural network. Convolutional neural networks perform well when diverse and sufficient amounts of data are supported. However, medical data is not easy to access and it is difficult to gather enough high-quality data that includes expert annotations. So This paper evaluates the efficacy of transfer learning in gastroscopy classification and diagnosis. We obtained 787 endoscopic images of gastric endoscopy at Gil Medical Center, Gachon University. The number of normal images was 200, and the number of abnormal images was 587. The image size was reconstructed and normalized. In the case of the ResNet50 structure, the classification accuracy before and after applying the transfer learning was improved from 0.9 to 0.947, and the AUC was also improved from 0.94 to 0.98. In the case of the InceptionV3 structure, the classification accuracy before and after applying the transfer learning was improved from 0.862 to 0.924, and the AUC was also improved from 0.89 to 0.97. In the case of the VGG16 structure, the classification accuracy before and after applying the transfer learning was improved from 0.87 to 0.938, and the AUC was also improved from 0.89 to 0.98. The difference in the performance of the CNN model before and after transfer learning was statistically significant when confirmed by T-test (p < 0.05). As a result, transfer learning is judged to be an effective method of medical data that is difficult to collect good quality data.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.655-657
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• 2021

인공지능 알고리즘을 이용한 유방암의 조기진단에 관련된 연구는 최근들어 활발하게 진행되고 있으나, 사용자의 목적에 맞는 처리속도 및 정확도 등에 다양한 한계점을 보인다. 이러한 문제를 해결하기 위해, 본 논문에서는 ImageNet에서 학습된 ResNet 모델을 현미경 기반 암세포 이미지에서 활용이 가능한 다단계 전이 학습을 제안하고, 이를 다시 전이 학습하여 초음파 유방암 영상을 양성 및 악성으로 분류하는 실험을 진행하였다. 제안된 다단계 전이 학습 알고리즘은 초음파 유방암 영상을 분류하였을 때 96% 이상의 정확도를 보였으며, 향후 암 세포주 및 실시간 영상처리 등의 추가를 통해 보다 높은 활용도와 정확도를 보일 것으로 기대한다.

• 문화기술의 융합
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• 제9권2호
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• pp.569-574
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• 2023

콘크리트 구조물의 적절한 유지관리를 위해서 다양한 결함에 대해 사전에 파악하고 유지관리하는 것이 필요하다. 현재 방법으로는 규모가 큰 사회기반시설물의 점검 시 효율성, 안전성, 신뢰성에 문제가 있어 새로운 점검 방식의 도입이 필요하다. 최근에는 영상에 대한 딥러닝 기술이 발달함에 따라 콘크리트 결함 분류 연구가 활발히 진행되고 있다. 하지만, 균열 외에 오염과 박락 등에 대한 연구는 제한적이다. 본 연구에서는 사전에 학습된 딥러닝 모델에 대한 전이학습을 통한 다양한 콘크리트 결함 유형 분류 모델을 개발하고, 정확도를 저하시키는 요인을 도출 및 향후 발전 방향을 제시하였다. 이는 향후 콘크리트 유지관리 분야에서 활용도가 높을 것으로 예상된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2093-2108
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• 2017

In this paper, we describe a novel method for recognizing human actions from different views via view knowledge transfer. Our approach is characterized by two aspects: 1) We propose a unsupervised topic transfer model (TTM) to model two view-dependent vocabularies, where the original bag of visual words (BoVW) representation can be transferred into a bag of topics (BoT) representation. The higher-level BoT features, which can be shared across views, can connect action models for different views. 2) Our features make it possible to obtain a discriminative model of action under one view and categorize actions in another view. We tested our approach on the IXMAS data set, and the results are promising, given such a simple approach. In addition, we also demonstrate a supervised topic transfer model (STTM), which can combine transfer feature learning and discriminative classifier learning into one framework.

• 한국전문물리치료학회지
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• 제4권1호
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• pp.48-62
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• 1997

Several studies have evaluated the effects of types of relative frequency and delay interval of knowledge of results(KR) on motor skill learning independently. The purpose of this study was to determine more effective types of KR relative frequency and KR delay interval for motor learning. Forty-six healthy subjects (15 female, 31 male) with no previous experience with this experiment participated. The subjects ranged in age from 20 to 29 years (mean=23.9, SD=0.474). All subjects were assigned to one of four groups: a high-instant group, a high-delay group, a low-instant group, and a low-delay group. During the acquisition phase, subjects practiced movements to a target (400 mm) with either a high (83%) or low (33%) KR relative frequency, and with either an instantaneous or delayed (after 8s) KR. Four groups were evaluated on retention (after 3min and 24hr) and transfer (450 mm) tests. The major findings were as follows: (1) there were no between-group differences in acquisition and short-term retention (p>0.05, (2) a low (33%) KR relative frequency during practice was as effective for learning as measured by both long-tenn retention and transfer tests, compared with high (83%) KR practice conditions (p<0.05), (3) delayed (8s) KR enhanced learning as measured by both long-term retention and transfer tests, compared with instantaneous KR practice conditions (p<0.05), and (4) there were no interactions between KR relative frequency and KR delay interval during acquisition, retention, and transfer phases. The results suggest that relatively less frequent and delayed KR are more effective types for motor learning than more frequent and instantaneous KR.

• 한국환경성돌연변이발암원학회:학술대회논문집
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• 한국환경성돌연변이발암원학회 2004년도 KSOT/KEMS Fall Annual Meeting
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• pp.153-153
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• 2004