• 한국컴퓨터정보학회논문지
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• 제21권3호
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• pp.97-104
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• 2016

In this paper, we propose spatial information based simulator for user experience optimization and minimize real space complexity. We focus on developing simulator how to design virtual space model and to implement virtual character using real space data. Especially, we use expanded events-driven inference model for SVM based on machine learning. Our simulator is capable of feature selection by k-fold cross validation method for optimization of data learning. This strategy efficiently throughput of executing inference of user behavior feature by virtual space model. Thus, we aim to develop the user experience optimization system for people to facilitate mapping as the first step toward to daily life data inference. Methodologically, we focus on user behavior and space modeling for implement virtual space.

• 아태비즈니스연구
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• 제13권3호
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• pp.1-18
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• 2022

Purpose - The purpose of this study is to take fitness APP users as the research object from the perspective of user experience to explore the influence of fitness APP user experience factors on product trust and purchase intention. Design/methodology/approach - The study collected data on 275 customers who had experience buying and using fitness apps. To test the hypothesis, SPSS 27.0 and AMOS 26.0 statistical packages were used based on the collected data. Findings - The results showed that the user experience factors (usefulness, ease to use, enjoyment, interaction) of fitness APP and the relationship between product trust had a positive effect, and product trust had a positive effect on purchase intention. In addition, exercise experience, showed a moderating effect in the relationship between the usefulness, easy to use of user experience and product trust. Research implications or Originality - This study provided research model among user experience factors of fitness APP, product trust and purchase intention. This study can help sports and fitness companies with product optimization and marketing decisions.

• 한국융합학회논문지
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• 제12권4호
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• pp.125-133
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• 2021

체험의 시대를 맞아 사용자 체험은 다양한 분야에서 주목받고 있으며 사용자 체험의 중요성을 강조하기 시작하였다. 본 논문은 스마트폰 의료 앱의 사용자 체험에서 중요한 요인이 무엇인지 분석하며 이러한 요인들의 상대적 중요도를 평가함으로써 의료 앱을 개발 시 우선시해야 할 사항에 대하여 제의를 하며 의료 엡 디자인의 최적화 및 서비스 품질 개선에 참고로 체공하고자 한다. 우선 사용자 체험 이론, 스마트폰 앱의 사용자 체험과 모바일 의료 앱에 관한 연구를 바탕으로 스마트폰 의료 앱 사용자 체험 요인을 정리한다. 다음으로 스마트폰 앱 다운로드 경험이 많고 의료 앱을 사용하는 20~40대 200명을 대상으로 설문조사를 실시하며 18가지 영향요인에 대하여 점수를 매긴다. 마지막으로 개발자가 새로운 앱을 개발할 경우 제품자원, 의료 광고 추천, 의사와 환자의 상호 작용성, 정서적 재미 유발, 응용 프로그램 학습 용이성 등의 영향요인이 사용자가 앱을 사용한 경우 좋은 체험을 얻게 하는 데 큰 영향을 미친다는 것을 알았다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.4025-4041
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• 2020

Mobile edge computing (MEC) is capable of providing services to smart devices nearby through radio access networks and thus improving service experience of users. In this paper, an offloading strategy for the joint optimization of computing and communication resources in multi-user and multi-MEC overlapping scene was proposed. In addition, under the condition that wireless transmission resources and MEC computing resources were limited and task completion delay was within the maximum tolerance time, the optimization problem of minimizing energy consumption of all users was created, which was then further divided into two subproblems, i.e. offloading strategy and resource allocation. These two subproblems were then solved by the game theory and Lagrangian function to obtain the optimal task offloading strategy and resource allocation plan, and the Nash equilibrium of user offloading strategy games and convex optimization of resource allocation were proved. The simulation results showed that the proposed algorithm could effectively reduce the energy consumption of users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.1912-1931
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• 2018

In future wireless network, user experience and energy efficiency will play more and more important roles in the communication systems compared to their roles at present. Quality of experience (QoE) and Energy Efficiency (EE) become the widely used metrics. In this paper, we study a combinatorial problem of QoE and EE and investigate a fair power allocation in heterogeneous networks. We first design a new metric, QoE-aware EE (QEE) to reflect the relationship of QoE and energy. Then, the concept of Utopia QEE is introduced, which is defined as the achievable maximum QEE in ideal conditions, for each user. Finally, we transform the power allocation process to an optimization of ratio of QEE and Utopia QEE and use invasive weed optimization (IWO) algorithm to solve the optimization problem. Numerical simulation results indicate that the proposed algorithm can get converged and efficiently improve the system energy efficiency and the QoE for each user.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.4680-4697
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• 2017

In future, since the user experience plays a more and more important role in the development of today's communication systems, quality of experience (QoE) becomes a widely used metric, which reflects the subjective experience of end users for wireless service. In addition, the energy efficiency is an increasingly important problem with the explosive growth in the amount of wireless terminals and nodes. Hence, a QoE-aware energy efficiency maximization based joint user access selection and power allocation approach is proposed to solve the problem. We transform the joint allocation process to an optimization of energy efficiency by establishing an energy efficiency model, and then the optimization problem is solved by chaotic clone immune algorithm (CCIA). Numerical simulation results indicate that the proposed algorithm can efficiently and reliably improve the QoE and ensure high energy efficiency of networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1238-1259
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• 2024

Previous studies on joint optimization of computation offloading and service caching policies in Mobile Edge Computing (MEC) have often neglected the impact of dependency-aware subtasks, edge server resource constraints, and multiple users on policy formulation. To remedy this deficiency, this paper proposes a many-objective joint optimization dependency-aware task offloading and service caching model (MaJDTOSC). MaJDTOSC considers the impact of dependencies between subtasks on the joint optimization problem of task offloading and service caching in multi-user, resource-constrained MEC scenarios, and takes the task completion time, energy consumption, subtask hit rate, load variability, and storage resource utilization as optimization objectives. Meanwhile, in order to better solve MaJDTOSC, a many-objective evolutionary algorithm TSMSNSGAIII based on a three-stage mating selection strategy is proposed. Simulation results show that TSMSNSGAIII exhibits an excellent and stable performance in solving MaJDTOSC with different number of users setting and can converge faster. Therefore, it is believed that TSMSNSGAIII can provide appropriate sub-task offloading and service caching strategies in multi-user and resource-constrained MEC scenarios, which can greatly improve the system offloading efficiency and enhance the user experience.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.2233-2252
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• 2018

For the problem of cross-layer joint resource allocation (JRA) in the Long-Term Evolution (LTE)-Advanced standard using carrier aggregation (CA) technology, it is difficult to obtain the optimal resource allocation scheme. This paper proposes a joint resource allocation algorithm based on the weights of user's average quality of experience (JRA-WQOE). In contrast to prevalent algorithms, the proposed method can satisfy the carrier aggregation abilities of different users and consider user fairness. An optimization model is established by considering the user quality of experience (QoE) with the aim of maximizing the total user rate. In this model, user QoE is quantified by the mean opinion score (MOS) model, where the average MOS value of users is defined as the weight factor of the optimization model. The JRA-WQOE algorithm consists of the iteration of two algorithms, a component carrier (CC) and resource block (RB) allocation algorithm called DABC-CCRBA and a subgradient power allocation algorithm called SPA. The former is used to dynamically allocate CC and RB for users with different carrier aggregation capacities, and the latter, which is based on the Lagrangian dual method, is used to optimize the power allocation process. Simulation results showed that the proposed JRA-WQOE algorithm has low computational complexity and fast convergence. Compared with existing algorithms, it affords obvious advantages such as improving the average throughput and fairness to users. With varying numbers of users and signal-to-noise ratios (SNRs), the proposed algorithm achieved higher average QoE values than prevalent algorithms.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제21권5호
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• pp.379-386
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• 2015

본 논문에서는 스마트폰 사용자 중심의 반응 완료 시간에 대한 동적 분석을 활용하여 사용자가 실제 인지하는 성능 중심의 새로운 최적화 프레임워크를 제안한다. 이를 위하여 먼저 스마트폰 응용프로그램에서 사용자가 실제 인지하는 성능에 대한 평가 지표로써 사용자 중심 반응 시간을 정의한다. 또한, 이러한 사용자 중심 반응 시간의 동적 탐색에 기반하여 사용자가 인지할 수 있는 성능 병목 지점을 최적화의 힌트로써 개발자에게 제공하는 사용자 중심 반응 시간 분석 도구의 설계와 개발에 대하여 소개한다. 제안한 사용자 중심 반응 시간 분석 도구를 갤럭시 넥서스 스마트폰에 구현하여 그 정확도와 계산부하를 평가한 결과, 전체 반응 시간의 1% 미만의 계산 부하로 카메라를 이용하여 측정한 결과 대비 92%의 정확도를 보였다. 제안한 도구의 효율성 평가를 위하여 소스 코드가 공개되어 있는 안드로이드 응용프로그램의 성능 개선에 제안한 도구를 활용하여 최대 16.4%의 성능 향상을 달성하였다.

• 한국콘텐츠학회논문지
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• 제16권2호
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• pp.14-24
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• 2016

최근 전기자동차와 관련된 콘텐츠 연구에서 사운드 디자인이 새로운 연구 주제로 부각되고 있다. 이 연구는 전기자동차 실내 주행음 디자인의 주요 가치들을 사용자 경험(User Experience) 측면에서 탐색하고 검증하는 것을 목적으로 진행되었다. 근거이론에 의한 탐색적 연구를 통해 맥락정보성과 정숙성을 독립변수로 도출하여 $2{\times}2$ 요인 설계로 유용성, 감성, 만족도에 대한 주효과 및 두 변인 간 상호작용 효과를 분석하였다. 실험연구를 통해 맥락정보성과 정숙성의 조합에 따라 전기 자동차의 사용자 경험에 미치는 영향이 달라진다는 점을 밝혔다. 연구 결과에 기반하여 실내 주행 사운드 가치의 개인별 선호를 고려한 사용자 니즈 최적화를 위한 후속 연구 방향을 제시하였다.