• Journal of Korea Game Society
• /
• v.19 no.2
• /
• pp.135-144
• /
• 2019

This study examined the role of enjoyment in the relationship between perceived auditory feedback (PAF), which provides information about the situation during game play, and user experience factors. 100 undergraduates played a mobile game, 'Classy Royale' and took a survey about their user experience. As a result of analyzing the available data from 98 participants, PROCESS MACRO showed that PAF was positively associated with enjoyment, immersion, and intention to use; enjoyment mediated the effects of PAF on immersion and intention. These results can help game research and development regarding the functional value of audio factors.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39A no.1
• /
• pp.65-67
• /
• 2014

In this letter, the performance of the interference channel with continuously varying channel is evaluated by using interference alignment based on practical channel estimation and channel state information(CSI) feedback and ideal Doppler frequency estimation. In this paper, performance evaluation is performed in terms of sum rate for 3-user interference channel. And also, sum rate is measured according to frequency of channel estimation relating with the calculation complexity. Simulation results show that the proposed scheme outperforms the conventional one which assumes that the channel is constant in a frame in some circumstances.

• Journal of IKEEE
• /
• v.25 no.3
• /
• pp.420-424
• /
• 2021

The massive MIMO system, which is a core technology of 5G communication systems, has a problem that it is difficult to implement in a frequency division duplex system based on limited channel feedback because a large amount of channel information is required at the transmitting end. In order to solve this problem, the Joint Spatial Division and Multiplexing (JSDM) technique that dramatically reduces the channel information requirement by removing interference between the user groups using channel correlation information that does not change for a long time has been proposed. Recently, a regularized JSDM technique has been proposed to further improve performance by allowing residual interference between the user groups. However, such JSDM-related studies were mainly designed to focus on inter-group interference cancellation, and thus performance analysis was not performed in a more realistic environment assuming limited feedback in the intra-group interference cancellation phase. In this paper, we analyze the performance of the JSDM and regularized JSDM techniques according to the number of groups and users in a limited feedback environment, and through the simulation results, demonstrate that the regularized JSDM technique shows a more remarkable advantage compared to the existing JSDM in a limited feedback environments.

• Journal of Internet Computing and Services
• /
• v.24 no.6
• /
• pp.23-29
• /
• 2023

As an effort to maximize the immersiveness of user experience in virtual reality, there have been constant efforts to provide a user with tactile sensation by providing haptic feedback. Most of the haptic feedback methods, however, can create only limited or unrealistic haptic sensations since they utilize affordable actuators such as a vibrotactile actuator. When it comes to martial arts training or a game, the limitation of such haptic feedback is apparent due to the significant difference between the physical impact of hitting an object and the sensation departed from a vibrotactile actuator. Noting this, we proposed a haptic impact system that can create a haptic impact when the user hits a virtual object with the fist. The haptic interface uses a quick-return mechanism that can deliver haptic impact feedback to a user's fist. The realism of the haptic impact was evaluated by conducting a human-subject experiment. The results indicate a significant effect of haptic feedback on the realism of the virtual impact.

• Journal of Internet Computing and Services
• /
• v.1 no.2
• /
• pp.89-96
• /
• 2000

Relevance feedback is a technique to learn the user's subjective perception of similarity between images, and has recently gained attention in Content Based Image Retrieval. Most relevance feedback methods assume that the individual features that are used in similarity judgments do not interact with each other. However, this assumption severely limits the types of similarity judgments that can be modeled In this paper, we explore a more sophisticated model for similarity judgments based on fuzzy measures and the Choquet Integral, and propose a suitable algorithm for relevance feedback, Experimental results show that the proposed method is preferable to traditional weighted- average techniques.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.24 no.4
• /
• pp.91-98
• /
• 2013

The purpose of this study is to examine the effect of force feedback on video gamers' performance with two different types of game controllers, and to verify users' consistency on their perceptions of force feedback effect and their actual performances in video games. 42 qualified players' records on a driving video game were analyzed in this study. This study shows 1) the force feedback effect in video games depends on the type of game controllers, 2) there is inconsistency between gamers' perception on the force feedback effect and their actual performances with the force feedback effect, and 3) emotional pleasure (fun factor) plays a big role in gamers' performances.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1302-1306
• /
• 2004

This paper proposes a model for information filtering (IF) on the Web. The user information need is described into two levels in this model: profiles on category level, and Boolean queries on document level. To efficiently estimate the relevance between the user information need and documents by fuzzy, the user information need is treated as a rough set on the space of documents. The rough set decision theory is used to classify the new documents according to the user information need. In return for this, the new documents are divided into three parts: positive region, boundary region, and negative region. We modified user profile by the user's relevance feedback and discerning words in the documents. In experimental we compared the results of three methods, firstly is to search documents that are not passed the filtering system. Second, search documents that passed the filtering system. Lastly, search documents after modified user profile. The result from using these techniques can obtain higher precision.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.9
• /
• pp.4398-4417
• /
• 2017

Multi-user Multiple-Input and Multiple-Output (MU-MIMO) has potential for prominently enhancing the capacity of wireless network by simultaneously transmitting to multiple users. User selection is an unavoidable problem which bottlenecks the gain of MU-MIMO to a great extent. Major state-of-the-art works are focusing on improving network throughput by using Channel State Information (CSI), however, the overhead of CSI feedback becomes unacceptable when the number of users is large. Some work does well in balancing tradeoff between complexity and achievable throughput but is lack of consideration of fairness. Current works universally ignore the rational utilizing of time resources, which may lead the improvements of network throughput to a standstill. In this paper, we propose TOUSE, a scalable and fair user selection scheme for MU-MIMO. The core design is dynamic-time-warping-based user selection mechanism for downlink MU-MIMO, which could make full use of concurrent transmitting time. TOUSE also presents a novel data-rate estimation method without any CSI feedback, providing supports for user selections. Simulation result shows that TOUSE significantly outperforms traditional contention-based user selection schemes in both throughput and fairness in an indoor condition.

• Journal of KIISE:Databases
• /
• v.36 no.6
• /
• pp.434-445
• /
• 2009

Content-based image retrieval(CBIR) is the retrieval technique which uses the contents of images. However, in contrast to text data, multimedia data are ambiguous and there is a big difference between system's low-level representation and human's high-level concept. So it doesn't always mean that near points in the vector space are similar to user. We call this the semantic-gap problem. Due to this problem, performance of image retrieval is not good. To solve this problem, the relevance feedback(RF) which uses user's feedback information is used. But existing RF doesn't consider user's region-of-interest(ROI), and therefore, irrelevant regions are used in computing new query points. Because the system doesn't know user's ROI, RF is proceeded in the image-level. We propose a new ROI RF method which guides a user to select ROI from relevant images for the retrieval of complex satellite image, and this improves the accuracy of the image retrieval by computing more accurate query points in this paper. Also we propose a pruning technique which improves the accuracy of the image retrieval by using images not selected by the user in this paper. Experiments show the efficiency of the proposed ROI RF and the pruning technique.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.8
• /
• pp.4270-4284
• /
• 2019

This study presents a reference model (RM) and the architecture of a cognitive health advisor (CHA) that integrates information with ambient intelligence. By controlling the information using the CHA platform, the reference model can provide various ambient intelligent solutions to a user. Herein, a novel approach to a CHA RM based on evolutional cyber-physical systems is proposed. The objective of the CHA RM is to improve personal health by managing data integration from many devices as well as conduct a new feedback cycle, which includes training and consulting to improve quality of life. The RM can provide an overview of the basis for implementing concrete software architectures. The proposed RM provides a standardized clarification for developers and service designers in the design and implementation process. The CHA RM provides a new approach to developing a digital healthcare model that includes integrated systems, subsystems, and components. New features for chatbots and feedback functions set the position of the conversational interface system to improve human health by integrating information, analytics, and decisions and feedback as an advisor on the CHA platform.