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      • KCI등재

        한국어 ‘를’ 중출문의 원인 연구

        최지화 ( Cui Zhihua ) 한국정치사회연구소 2023 한국과 국제사회 Vol.7 No.6

        ‘를’ 중출문 구조는 한국어에 존재하는 특색 있는 일종의 언어 구조 형식이다. 본고는 ‘를’의 통사·의미론적 기능과 화용론적 기능을 고찰함으로써 ‘를’의 기능을 밝히고 그 역할을 정립하며 ‘를’ 중출문의 원인을 살펴보는 데 목적이 있다. 본고에서 ‘를’ 중출문의 유형은 박성미(2014)가 분류한 유형을 기준으로 삼고 동족목적어 중출문을 보충해서 각 중출문의 원인에 대해서 분석했다. 본고의 논의는 기존에 ‘를’을 격조사로 볼 것인지, 보조사로 볼 것인지에 대한 이분법적 논의에 시사하는 바가 크다. 격조사 ‘를’과 보조사 ‘를’은 상호 배타적인 것이 아니며, 보조사 ‘를’의 의미는 격조사 ‘를’이 갖는 담화 층위의 초점 의미와 다르지 않다는 점이 그것이다. 나아가 본고의 논의는 ‘를’이 격조사의 기능을 하되 때에 따라 ‘강조’ 의미의 보조사적 쓰임도 있다는 다의어적 입장을 뒷받침해줄 수 있다고 본다. 본고는 ‘를’의 대격 조사로서의 역할 및 초점 표지로서의 역할을 정립할 수 있었다는 점에서 의의가 있다. This paper explains the function of “를”, establishes its role, and explores the reasons for the “를” double occurrence by examining the general meaning, semantic function, and pragmatic function of “를”. In this paper, the types of “를” double occurrence sentences are based on the classifications made by Park Seong-Mi (2014). Additionally, this study supplements the analysis with co-referential object double occurrence sentences, examining the causes of each double occurrence sentence. This paper establishes the role of “를” as a pronoun auxiliary and as a focus marker, which is of great significance. The case-auxiliary “를” and the auxiliary word “를” are not completely exclusive, and the meaning of the auxiliary word 를 does not differ from the focus marker of the conversational hierarchy that the case-auxiliary “를” has. Furthermore, the discussion in this paper supports the polysemous position that “를” has the function of the case-auxiliary and also can be used as an auxiliary word of “emphasizing” meaning.

      • KCI등재

        Optimal LEACH Protocol with Improved Bat Algorithm in Wireless Sensor Networks

        ( Xingjuan Cai ),( Youqiang Sun ),( Zhihua Cui ),( Wensheng Zhang ),( Jinjun Chen ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.5

        A low-energy adaptive clustering hierarchy (LEACH) protocol is a low-power adaptive cluster routing protocol which was proposed by MIT’s Chandrakasan for sensor networks. In the LEACH protocol, the selection mode of cluster-head nodes is a random selection of cycles, which may result in uneven distribution of nodal energy and reduce the lifetime of the entire network. Hence, we propose a new selection method to enhance the lifetime of network, in this selection function, the energy consumed between nodes in the clusters and the power consumed by the transfer between the cluster head and the base station are considered at the same time. Meanwhile, the improved FTBA algorithm integrating the curve strategy is proposed to enhance local and global search capabilities. Then we combine the improved BA with LEACH, and use the intelligent algorithm to select the cluster head. Experiment results show that the improved BA has stronger optimization ability than other optimization algorithms, which the method we proposed (FTBA-TC-LEACH) is superior than the LEACH and LEACH with standard BA (SBA-LEACH). The FTBA-TC-LEACH can obviously reduce network energy consumption and enhance the lifetime of wireless sensor networks (WSNs).

      • KCI등재

        A Bi-objective Game-based Task Scheduling Method in Cloud Computing Environment

        Wanwan Guo,Mengkai Zhao,Zhihua Cui,Liping Xie 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.11

        The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.

      • KCI등재

        Multi-factor Evolution for Large-scale Multi-objective Cloud Task Scheduling

        Tianhao Zhao,Linjie Wu,Di Wu,Jianwei Li,Zhihua Cui 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.4

        Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large-scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

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