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알람기르,신진교,최재혁 한국산업경영학회 2022 경영연구 Vol.37 No.4
Social media is playing a significant role in organizational communication, and LMX is contributing to strong relationships with leaders and members who contribute to organizational innovation. This research paper aims to research whether social media is weakening LMX for organizational innovation. This paper analyses 217 sample data gathered through convenient and snowballed sampling, and SPSS & AMOS have been used for structural equation modeling to test the hypotheses. The hypothesis test has proved that social media do not significantly affect organizational innovation and whether LMX has a partial mediating effect between social media and organizational innovations. This study contributes to the social exchange literature and empirical test of the structural model where social media communication directly affects organization innovation, and LMX has full mediation. Therefore, LMX is not very much strong in developing countries like Bangladesh, and social media is to weakening it. In other words, social media seem important for innovation, but LMX still has high value for organizational innovation and management. So in the era of digital technology, social media supports horizontal communication, and leader-member exchange for vertical communication is necessary for organizational innovation. This research has observed some limitations in the analysis of constructs. Here observed variables are limited only to social media, LMX, and organizational innovations. In that case is a scope to find out social media’s impact on organizational innovation through multiple samples like responses from leaders and members, considering other variables like Top management support and IT support as mediators. In addition, this model can be tested in developing countries with organizational recognition using social media. Lastly, this paper adds value to communication theory focusing on the importance of LMX and social media for organizational innovations in this technological era.
Effective Image Segmentation using a Locally Weighted Fuzzy C-Means Clustering
나이마 알람저(Nyma Alamgir),김종면(Jong-Myon Kim) 한국컴퓨터정보학회 2012 韓國컴퓨터情報學會論文誌 Vol.17 No.12
본 논문에서는 기존의 퍼지 클러스터링 기반 이미지 분할의 성능과 계산 효율을 개선하기 위해 퍼지 클러스터링의 목적 함수를 수정하는 이미지 분할 프레임워크를 제안한다. 제안하는 이미지 분할 프레임워크는 주변 픽셀들에 가중치를 부여함으로써 현재 센터 픽셀 연산을 위해 주변 픽셀들의 중요성을 고려하는 지역 가중치 적용 퍼지 클러스터링 기법을 포함한다. 이러한 가중치들은 각 멤버쉽들의 중요성을 표시하기 위해 현재 픽셀과 대응되는 각 주변 픽셀들 사이의 거리차에 의해 결정되어 지며, 이러한 프로세서는 향상된 클러스터링 성능을 보장한다. 제안하는 방법의 성능을 평가하기 위해 분할 계수, 분할 엔트로피, Xie-Bdni 함수, Fukuyzma-Sugeno 함수와 같은 네 가지 클러스터 유효성 함수를 이용하여 분석하였다. 모의실험 결과, 제안한 방법은 기존의 다른 퍼지 클러스터링 기법들보다 클러스터 유효성 함수들뿐만 아니라 분할과 조밀도 측면에서 우수한 성능을 보였다. This paper proposes an image segmentation framework that modifies the objective function of Fuzzy C-Means (FCM) to improve the performance and computational efficiency of the conventional FCM-based image segmentation. The proposed image segmentation framework includes a locally weighted fuzzy c-means (LWFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors. Distance between a center pixel and a neighboring pixels are calculated within a window and these are basis for determining weights to indicate the importance of the memberships as well as to improve the clustering performance. We analyzed the segmentation performance of the proposed method by utilizing four eminent cluster validity functions such as partition coefficient (V<sub>pc</sub>), partition entropy (V<sub>pe</sub>), Xie-Bdni function (V<sub>xb</sub>) and Fukuyama-Sugeno function (V<sub>fs</sub>). Experimental results show that the proposed LWFCM outperforms other FCM algorithms (FCM, modified FCM, and spatial FCM, FCM with locally weighted information, fast generation FCM) in the cluster validity functions as well as both compactness and separation.
배경 분리 알고리즘 기반 이동 객체 탐지 성능 평가 기법 연구
호씬 엠디 알람깃,호씬 엠디 임티아즈,호씬 엠디 딜로와르,이가원,허의남 한국정보과학회 2020 정보과학회 컴퓨팅의 실제 논문지 Vol.26 No.10
The background subtraction technique finds moving objects and reconstructs the background from video sequences. The background subtraction has extensive real-world applications. Most of the background subtraction studies have focused on increasing the accuracy while reducing the complexity. Though few studies have appraised the accuracy of the background subtraction methods, the researchers have not measured the computational complexity of the methods. Thus, in this study, our main goal was to measure the accuracy and computational complexity of the background subtraction approaches. This study can be used in industry and academy. Also, we implemented and assessed the performance of the three different types of background subtraction algorithms such as the cluster-based method, the statistical-based method, and the sample consensusbased method. We mainly used the F-measure with other confusion metrics, which are the most accepted criteria to assess the segmentation accuracy of the background subtraction algorithms. Also, we evaluated the complexity in terms of the memory usage per pixel and the number of frame display per second for the CDD-2012, CDD-2014, and Carnegie Mellon datasets. The experimental data are presented in the table in Section 4 to show the accuracy and computational complexity. 배경 분리 알고리즘은 비디오 시퀀스에서 배경을 재구성하여 움직이는 객체를 찾아내기 위한 기술로, 광범위한 응용 분야에 활용되고 있다. 배경 분리 알고리즘의 연구는 주로 복잡도를 줄이면서 정확성을 높이는 데 중점을 두고 있으나, 복잡도를 측정하거나 정확도를 평가하는 방법에 대한 연구는 상대적으로 미비한 상태이다. 따라서, 본 연구에서는 산업 및 학술적으로 모두 사용 가능한 배경 분리 알고리즘의 정확도와 계산 복잡도 평가 방안을 제시하고자 한다. 본 논문에서는 클러스터 기반 기법, 통계 기반 기법, 표본 합의 기반 기법의 세가지 종류와 배경 분리 알고리즘을 구현하고 평가한다. 특히 배경 분리 알고리즘의 분할 정확도를 평가하는데 가장 적합한 방법인 F-measure를 다른 혼합 지표와 함께 사용하였으며, CDD-2012, CDD-2014, 카네기멜론의 데이터시트를 픽셀당 메모리 사용량, 초당 프레임 수로 복잡도를 평가하여 정확도와 계산 복잡도를 나타낸 실험 결과를 본문(섹션 4)에 제시하였다.