• 한국ITS학회 논문지
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• 제22권1호
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• pp.81-92
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• 2023

교통상황 분류는 신호연동그룹 단위의 정주기식 제어 기법을 효율적으로 적용하기 위하여 TOD 계획을 수립하는데 핵심적인 기술이다. 본 논문에서는 신호연동그룹에 속하는 모든 교차로의 교통 자료 즉, 속도-교통량-밀도를 활용할 수 있는 딥 임베디드 클러스터링(Deep-Embedded Clustering:DEC) 기반 교통상황 분류 방법론을 제시하였다. 기존의 신호계획의 경우 교통량 기반으로 주요 교차로를 선정하고 해당 교차로의 교통자료를 이용하여 단편적인 신호계획을 하였으나, 본 논문에서 제시된 방법론의 경우 신호연동 그룹내 다수 교차로의 종합적인 교통특성에 따라 교통상황을 유연하게 분류하여 신호계획을 할 수 있는 기반을 제공하였다. 본 연구에서 제시된 방법론은 일반적인 군집화 방법론이 입력 자료의 차원이 증가함에 따라 겪는 차원의 저주 (Curse of dimensionality) 문제를 완화함으로써 신호연동그룹에 속하는 모든 신호교차로의 교통자료를 고려한 신호시간 계획 수립이 가능하며 기존의 특정교차로 및 교통량만을 이용한 교통상황 분류방법론의 단점을 극복할 수 있음을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1620-1634
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• 2023

This paper studies the task of embedding nodes with multiple graphs representing multiple information channels, which is useful in a large volume of network clustering tasks. By learning a node using multiple graphs, various characteristics of the node can be represented and embedded stably. Existing studies using multi-channel networks have been conducted by integrating heterogeneous graphs or limiting common nodes appearing in multiple graphs to have similar embeddings. Although these methods effectively represent nodes, it also has limitations by assuming that all networks provide the same amount of information. This paper proposes a method to overcome these limitations; The proposed method gives different weights according to the source graph when embedding nodes; the characteristics of the graph with more important information can be reflected more in the node. To this end, a novel method incorporating a multi-channel gate layer is proposed to weigh more important channels and ignore unnecessary data to embed a node with multiple graphs. Empirical experiments demonstrate the effectiveness of the proposed multi-channel-based embedding methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3836-3854
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• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.