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Toan Khanh Pham TRAN 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.8
The emerging competitive environment in today’s global marketplace is one where businesses no longer compete with each other as autonomous, individual firms. Global, economic, and technological development pressure forces organizations to continually enhance their performance through knowledge sharing and innovativeness. The purpose of this paper is to explore the moderating role of knowledge sharing and the mediating role of innovativeness in the relationship between transformational leadership and organizational performance. The data was collected from 389 employees in Vietnamese industrial enterprises through a questionnaire survey. The information was then analyzed by explanatory factor analysis (EFA) confirmatory factor analysis (CFA) as well as structural equation modeling (SEM). The results show that the mediating role of innovativeness and the moderating role of knowledge sharing in the relationship between transformational leadership and performance, are supported. Organizations may reap the benefits of an innovative workforce by selecting, nurturing transformational leaders. This study contributes to the field of human resources management, particularly leadership, by exploring the role of transformational leadership. Moreover, this is the first study to test the moderating role of knowledge sharing and the mediating role of innovativeness in the relationship between transformational leadership and the organizational performance.
마스크된 복원에서 질병 진단까지: 안저 영상을 위한 비전 트랜스포머 접근법
( Toan Duc Nguyen ),변규린 ( Gyurin Byun ),추현승 ( Hyunseung Choo ) 한국정보처리학회 2023 한국정보처리학회 학술대회논문집 Vol.30 No.2
In this paper, we introduce a pre-training method leveraging the capabilities of the Vision Transformer (ViT) for disease diagnosis in conventional Fundus images. Recognizing the need for effective representation learning in medical images, our method combines the Vision Transformer with a Masked Autoencoder to generate meaningful and pertinent image augmentations. During pre-training, the Masked Autoencoder produces an altered version of the original image, which serves as a positive pair. The Vision Transformer then employs contrastive learning techniques with this image pair to refine its weight parameters. Our experiments demonstrate that this dual-model approach harnesses the strengths of both the ViT and the Masked Autoencoder, resulting in robust and clinically relevant feature embeddings. Preliminary results suggest significant improvements in diagnostic accuracy, underscoring the potential of our methodology in enhancing automated disease diagnosis in fundus imaging.
Toan Van Pham,Vinh Mai Phuoc,Dung Van Nguyen,Jiro Koyama 대한환경공학회 2021 Environmental Engineering Research Vol.26 No.6
Pesticide containing wastewater is concerned due to their toxicity to human health and the environment, and therefore, is attracted much attention by public communities and scientists. This type of wastewater can be treated by conventional treatment methods including physical, chemical, biological methods and so on. Unfortunately, the efficiency of these methods are mostly undesired as expectation because they cannot completely remove toxic organic pollutants from wastewater. In this study, an experiment on laboratory scale model of cold plasma technology, a well-known water treatment method in recent decades, was performed to assess its efficiency on removing pesticide fenobucarb from the wastewater. Furthermore, another experiment on laboratory scale model of the cold plasma combined with coagulation/flocculation and ion exchange process was implemented to assess the efficiency of these combination and each process as well on treatment of pesticide containing wastewater. Experimental results demonstrated that the model of technologies combined was high effective in removing organic pollutants from wastewater. However, the specific efficiency of cold plasma technology in treating wastewater was low. Treatment time, energy supply and wastewater characteristics are the factors which are necessary to be further studied for cold plasma technology application.
Flexible MPEG-4 to H.264 Transcoder for Video Transmission with Effective Motion Information Reusing
Toan Nguyen Dinh,JaeMyung Yoo,Sungchan Park,GueeSang Lee,Young Chang,Han-Jin Cho 대한전자공학회 2007 ITC-CSCC :International Technical Conference on Ci Vol.2007 No.7
In this paper, we present a flexible MPEG-4@SP (with Simple Profile) to H.264 transcoder for transmitting video over a system which has different kinds of networks, various types of devices and video formats. From decision modes and motion vectors in MPEG-4 decoder, a set of prediction motion vectors (PMV) and limited decision modes are considered in H.264 encoder to reduce the transcoding time. To maintain a reasonable video quality, we apply an adaptive integer search range based on video picture characteristics to find the optimal motion vectors. We also present a novel spatial downscaling transcoding method in the transcoder for reducing bit rate and spatial resolution to meet the constrained transmission bandwidths and terminal devices capabilities. The simulation results show that our transcoder considerably reduces transcoding time compare to other methods while video quality is kept almost optimal.
텐서보팅을 이용한 K-means 군집화의 초기 파라메터 결정
Toan Nguyen,박종현(Jonghyun Park),이귀상(GueeSang Lee) 한국정보과학회 2009 한국정보과학회 학술발표논문집 Vol.36 No.2A
The major problem in K-means clustering is to determine the number of clusters and their starting centroids. The quality of the resulting clusters is largely dependent on the estimation of K. In this paper, we investigate a novel method based on tensor voting to estimate these initial parameters. (d+1)-D tensor voting is applied on d-dimensional data to generate a saliency map. Major peaks in the saliency map are detected to decide the number of clusters. Experimental results attained from 1-D, 2-D data points and in color image segmentation show that our method can estimate the number of clusters successfully. The proposed method can also solve some other issues in K-means clustering such as empty clusters and outliers. Empty clusters can be obtained if no points are allocated to a cluster during the assignment step. And when outliers are present, the resulting cluster centroids may not be as representative as they otherwise would be.