• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.2
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• pp.232-249
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• 2022

As the postsecondary school-age population continues to decline, the competition among universities to attract potential students has intensified. As an alternative, we propose to introduce a collaborative marketing strategy to universities to gain the attention of precollege students and parents. This study examines perceived fit, the prestige of university and fashion brands, consumption values, and the category of fashion brands in the context of collaboration between university identity and fashion brands. Utilizing an online survey, we collected 391 responses. The results indicate that perceived fit between universities and fashion brands has a significant impact on the purchase intention of collaborative university merchandise. In addition, the prestige of fashion brands plays a key role, while the prestige of universities has no direct effect on purchase intention. However, the indirect effect of university prestige on purchase intention mediated by perceived fit is significant. Also, this study confirms that social value and emotional value have significant impacts on purchase intention. These findings present a guideline for selecting a collaborative partner, which is the most important task in a collaboration strategy. Finally, merchandising strategies reflected consumption values based on precollege students and their parents' needs are proposed.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.208-222
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• 2022

Through the analysis of the coordination mechanism of the supply chain system of China's automobile manufacturing industry, the factors affecting the supply subsystem, the manufacturing subsystem, the sales subsystem, and the consumption subsystem are sorted out, the supply chain coordination index system based on the influence factor of four subsystems is established. The evaluation models of the coordination degree in the subsystem of the supply chain, the coordination degree among the subsystems, and the comprehensive coordination degree are established by using the efficiency coefficient method and the collaborative entropy method. Experimental results verify the accuracy of the evaluation model using the empirical analysis of the collaborative evaluation index data of China's automobile manufacturing industry from 2000 to 2019. The supply chain synergy of automobile manufacturing industry was low from 2001 to 2005, and it increased to a certain extent from 2006 to 2008 with a small growth rate from 0.10 to 0.15. From 2009 to 2013, the supply chain synergy of automobile manufacturing industry increased rapidly from 0.24 to 0.49, and it also increased rapidly but fluctuated from 2014 to 2019, first rising from 0.68 to 0.84 then dropping to 0.71. These results provide reference for the development of China's automobile manufacturing supply chain system and scientific decision-making basis for the formulation of relevant policies of the automobile manufacturing industry.

• Fashion & Textile Research Journal
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• v.24 no.1
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• pp.53-65
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• 2022

The fashion industry is facing numerous sustainability-related challenges due to growing consciousness about the egregious extent of global environmental problems. This study examines research trends on sustainable fashion based on five macro trends related to sustainable innovation in the fashion industry. Using the content analysis and network analysis methods, 115 research papers published in domestic and international journals from 2017 to 2021 were collected and analyzed. The study conclusions are as follows. First, majority of domestic papers(55.41%) focused on circular economy, whereas other topics such as consumer awareness(1.35%) and corporate social responsibility(2.70%), are yet to be thoroughly examined; majority of international papers(53.65%) focused on sharing economy and collaborative consumption, whereas other topics such as technological innovation(2.44%), are yet to be thoroughly examined. Second, domestic papers have found that many brands(68.57%) are applying the concept of circular economy, whereas international papers have found that many brands(51.56%) are applying the concept of sharing economy and collaborative consumption. The study results provide useful data for corporate management in the fashion industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1081-1087
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• 2015

Recently, the researches of aerial drones are actively executed in various areas, the researches of surface drones and underwater drones are also executed in marine areas. In case of surface drones, they essentially utilize acoustic information by the sonar and consequently have the local information in the obstacle avoidance as the sonar has the limitations due to the beam width and detection range. In order to overcome this, more global method that utilizes optical images by the camera is required. Related to this, the aerial drone with the camera is desirable as the obstacle detection of the surface drone with the camera is impossible in case of the existence of clutters. However, the dynamic-floating aerial drone is not desirable for the long-term operation as its power consumption is high. To solve this problem, a collaborative obstacle avoidance method based on the acoustic information by the sonar of the surface drone and the optical image by the camera of the static-floating aerial drone is proposed. To verify the performance of the proposed method, the collaborative obstacle avoidances of a MSD(Micro Surface Drone) with an OAS(Obstacle Avoidance Sonar) and a BMAD(Balloon-based Micro Aerial Drone) with a camera are executed. The test results show the possibility of real applications and the need for additional studies.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.77-88
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• 2021

The development history of China's big data is relatively short, and it has only been ten years so far. Although the application level of big data in real life is not high, some achievements have been made in the supply chain. Various kinds of data will be generated in the actual operation of the supply chain. If these data can be effectively classified and used, the "bullwhip effect" of the operation of the supply chain can be also effectively improved. Thus this paper proposes the development of a supply chain collaborative inventory management model and application framework using big data. In this study, we analyzed the supply chain of beer industry, which is the most prominent consumption industry with "bullwhip effect", and further established a big data collaborative inventory management model for the supply chain of beer industry based on system dynamics. We used the Vensim software for simulation and sensitivity test and after appling our model, we found that the inventory fluctuations of the participants in the beer industry supply chain became significantly smaller, which verified the effectiveness of the model. Our study can be also applied to the possible problems of the large data supply chain collaborative inventory management model, and gives certain countermeasures and suggestions.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.389-401
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• 2022

The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has caused problems of large mobile network communication delay and large energy loss. A strategy for resource allocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model of the cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offload tasks to MEC servers or neighboring vehicles for communication. Then, the communication model and the calculation model of IoV system are comprehensively analyzed. The optimization objective of minimizing delay and energy consumption is constructed. Finally, the on-board computing task is coded, and the optimization problem is transformed into a knapsack problem. The optimal resource allocation strategy is obtained through genetic algorithm. The simulation results based on the MATLAB platform show that: The proposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of system resources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an average delay of 210 ms, effectively reducing system overhead and improving response speed.

• Journal of Korean Home Economics Education Association
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• v.29 no.3
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• pp.89-104
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• 2017

Considering the advent of the Fourth Industrial Revolution, this study examines the changes and influences of intelligent information technology and the role of consumer education in the context of consumption life. The purpose of this study is to provide a theoretical foundation to effectively respond to the future consumption society as an independent consumer by enhancing the understanding of the Fourth Industrial Revolution in terms of consumption life. First, in terms of changes in the consumption paradigm in the Fourth Industrial Revolution, production and consumption are converged by being shared through a comprehensive connection platform in real time. Regarding the meaning of consumption, mental experience is being emphasized; moreover, usage and sharing, rather than ownership, are being highlighted. In terms of major changes in consumption life, the emergence of a more convenient smart consumption life and the possibility of personalized consumption optimized for individual demand are anticipated. Moreover, sustainable eco-friendly consumption is expected to increase further, and rapidly changing consumption trends will experience accelerated progress in consumer-centered changes. Next, the predicted problems in consumption life in the Fourth Industrial Revolution include unequal consumption due to intelligent information technology power center and the use and management of personal information data. Furthermore, ethical concerns related to the introduction of new technologies will become prominent, eventually resulting in issues concerning consumption satisfaction. To effectively respond to these new paradigm changes, consumer education should be value-centered. Ethical aspects of consumption should be considered, and consumption life should include trust and mutual cooperation. Furthermore, consumer education should facilitate creative convergence.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.181-188
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• 2018

In this paper, we propose an analysis scheme of customer spending pattern using text mining. In proposed consumption pattern analysis scheme, first we analyze user's rating similarity using Pearson correlation, second we analyze user's review similarity using TF-IDF cosine similarity, third we analyze the consistency of the rating and review using Sendiwordnet. And we select the nearest neighbors using rating similarity and review similarity, and provide the recommended list that is proper with consumption pattern. The precision of recommended list are 0.79 for the Pearson correlation, 0.73 for the TF-IDF, and 0.82 for the proposed consumption pattern. That is, the proposed consumption pattern analysis scheme can more accurately analyze consumption pattern because it uses both quantitative rating and qualitative reviews of consumers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.82-88
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• 2017

Recently recommendation methods need personalization and diversity as well as accuracy whereas the traditional researches have been mainly focused on the accuracy of recommendation in terms of quality. The diversity of recommendation is also important to people in terms of quantity in addition to quality since people's desire for content consumption have been stronger rapidly than past. In this paper, we pay attention to similarity of data gathered simultaneously among different types of contents. With this motivation, we propose an enhanced recommendation method using correlation analysis with considering data similarity between two types of contents which are movie and music. Specifically, we regard folksonomy tags for music as correlated data of genres for movie even though they are different attributes depend on their contents. That is, we make result of new recommendation movie items through mapping music folksonomy tags to movie genres in addition to the recommendation items from the typical collaborative filtering. We evaluate effectiveness of our method by experiments with real data set. As the result of experimentation, we found that the diversity of recommendation could be extended by considering data similarity between music contents and movie contents.