• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.221-228
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• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.476-492
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• 2022

This study explored at the potentials of exhibition appreciation classes using the metaverse platform, and identified what types of interactions could occur when elementary students participate in the exhibition appreciation education using metaverse platform. We had a user observation research and FGI through implementation of actual elementary class and we analyzed problematic experiences from the viewpoint of user to content and user to user interaction. As a result, fifteen UX improvements were derived by Delphi technique for solving the problematic experiences and improving learner's interactivity.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.606-607
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• 2015

The traffic volume on the road shows various trends reflecting regional characteristics for monthly and hourly, and economic loss affecting users varies according to the selected period for the maintenance of road pavements. Therefore, in this study, the user cost (or delay cost) for monthly and hourly on the work zone near the Maesang Bridge Section on Poseung-Gu, Pyeongtaek-si, Gyeonggi-do was calculated using the time series models and VISSIM, and based on the result, the effect of user cost reduction according to the selection of best maintenance period was examined. The analysis result showed that the month of the lowest user cost occurred due to the maintenance of target section was January (10,293,258 KRW/Day×1km) and the month of the greatest user cost occurred was November (13,337,495 KRW/Day×1km).

• Current Optics and Photonics
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• v.8 no.3
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• pp.307-312
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• 2024

Measuring the three-dimensional (3D) spatial light intensity distribution of an autostereoscopic multiview 3D display at the user position is time-consuming, as luminance has to be measured at different positions around the user position. This study investigates a method to quickly estimate the 3D distribution at the user position. For this purpose, a measurement setup using a white semitransparent diffusing screen or a two-dimensional (2D) spatial sensor was devised to measure the 2D light intensity distribution at the user position. Furthermore, the 3D spatial light intensity distribution at the user position was estimated from these 2D distributions at different viewing distances. From the estimated 3D distribution, the characteristics of autostereoscopic 3D display performance can be derived and the candidate positions for further accurate measurement can be quickly determined.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.1
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• pp.119-126
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• 2009

In recent years, many recommendation service systems that search or recommend information automatically considering user's taste or property are developed. However, there is a weak point that correct recommendation is hard without considering the preference of user's context. This paper proposes a filtering method that gives correct recommendation considering the preference of user's context. To support this method, we get UCOP(User-Context Object Preference) using the preference of user's context and Pearson correlation coefficient. The results of the experiment show the improvement of 11%, 2% of precision and 8%, 4% of recall comparing with the existing service systems. Our recommendation service systems show 77% of precision and 53% of recall overall.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.68-77
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• 2018

Regarding demand response (DR) by residential users (R-users), the users try to reduce electricity costs by adjusting their power consumption in response to the time-varying price. However, their power consumption may be affected not only by the price, but also by user convenience for using appliances. This paper proposes a methodology for appliance scheduling (AS) that considers the user convenience based on historical data. The usage pattern for appliances is first modeled applying the copula function or clustering method to evaluate user convenience. As the modeling results, the comfort distribution or representative scenarios are obtained, and then used to formulate a discomfort index (DI) to assess the degree of the user convenience. An AS optimization problem is formulated in terms of cost and DI. In the case study, various AS tasks are performed depending on the weights for cost and DI. The results show that user convenience has significant impacts on AS. The proposed methodology can contribute to induce more DR participation from R-users by reflecting properly user convenience to AS problem.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.3
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• pp.37-50
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• 2009

With the increase of number of internet services due to development of internet, it becomes a critical issue to appropriately manage the personal information about user's identity, that is ID information that service providers collect in terms of user's convenience improvement and privacy protection. But the existing ID information management models are service provider-centric or they insufficiently provide ID information management functions, so that they have not been satisfying user's requirement. In this paper, by establishing user-centric concept in we information management, we propose the new user-centric ID information management model in which user can make use of the internet service more conveniently and eventually enhance user's privacy.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.494-507
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• 2021

As service design becomes more common, various user research methods and tools such as cultural probes and contextual inquiry are being used to grasp the user's experience and needs. These methodologies have the advantage of being able to derive various insights through direct observation and investigation, but have disadvantages in that problems such as time and space constraints, cost occurrence, and delay in the service development process may occur. In addition, it is difficult to conduct actual observations, face-to-face investigations, and interviews due to COVID-19, which has recently spread rapidly around the world. Accordingly, prior to the digitization of cultural probes, I would like to suggest the direction of digital tools. First, after grasping the concept of service design and user research through literature research, the necessity of user research in service design was confirmed. After that, the necessity of digitalizing user research was confirmed by extracting functional elements that could be understood and digitalized about the cultural probes methodology, and analyzing previous studies using existing mobile-based survey techniques. Finally, we confirmed the direction of digital tools through user interviews through cultural probes.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.245-257
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• 2022

In the Internet of Things (IoT) era, the types of devices used by one user are becoming more diverse and the number of devices is also increasing. However, a forensic investigator is restricted to exploit or collect all the user's devices; there are legal issues (e.g., privacy, jurisdiction) and technical issues (e.g., computing resources, the increase in storage capacity). Therefore, in the digital forensics field, it has been a challenge to acquire information that remains on the devices that could not be collected, by analyzing the seized devices. In this study, we focus on the fact that multiple devices share data through account synchronization of the online platform. We propose a novel way of identifying the user's interest through analyzing the remnants of targeted advertising which is provided based on the visited websites or search terms of logged-in users. We introduce a detailed methodology to pick out the targeted advertising from cache data and infer the user's interest using deep learning. In this process, an improved learning model considering the unique characteristics of advertisement is implemented. The experimental result demonstrates that the proposed method can effectively identify the user interest even though only one device is examined.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1029-1036
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• 2021

Recently it has been proposed that lossless 2-user non-orthogonal multiple access (NOMA) can be implemented without successive interference cancellation (SIC) via correlated superposition coding (CSC). However only the 2-user case was considered. Thus this paper proposes a lossless 3-user non-SIC NOMA scheme. This can be achieved via the 3-user CSC scheme. Simulations show that the lossless 3-user non-SIC NOMA scheme can be implemented over power allocation ranges of user-fairness.