• 한국멀티미디어학회논문지
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• 제23권3호
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• pp.447-459
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• 2020

The Internet of Things(IoT) paradigm is flourishing strenuously for the last two decades. Researchers around the globe have their dreams to transmute every real-world object to the virtual object. Consequently, IoT devices are escalating exponentially. The abrupt evolution of these IoT devices has caused a major challenge i.e. object classification. In order to classify devices comprehensively and accurately, this paper proposes a context-aware based multi-classification model for devices, which classifies the smart devices according to people's contexts. However, the classification features of contextual data of different contexts are difficult to extract. The deep learning algorithm has the capability to solve this problem. This paper proposes a context-aware based multi-classification model of devices, which classifies the smart devices according to people's contexts.

• 한국항공운항학회지
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• 제19권1호
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• pp.101-107
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• 2011

A UAV is defined as a powered, aerial vehicle that does not carry a human operator, and can fly autonomously or be piloted remotely. UAV operations have increased dramatically during the past several years in both the public and private sectors. The utilization of UAV and the activities of diverse widening, now the challenge was how to operate and integrate UAV safely in the NAS. The purpose of this study is to look around the trend for operability of Smart UAV in the NAS and to provide its implications and the future direction of integrated operating airspace focusing on U.S. where R&D and demand of UAV are the most in the world.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2434-2454
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• 2021

This paper developed the software testing factory-cloud testing platform (STF-CTP) to address the software compatible issues in various smart devices. Software developers who only require uploading the application under test (AUT) and test script can test plenty of smart devices in STF-CTP. The challenge for the cloud test platform is how to optimize the resource and increase the performance in the limited resource. This paper proposed a new scheduling mechanism and a new process of the system operation which is based on the OpenStack platform. We decrease about 40% memory usage of OpenStack server, increase 3% to 10% Android device usage of STF-CTP, enhance about 80% test job throughput and reduces about 40% test job average waiting time.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2022년도 제65차 동계학술대회논문집 30권1호
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• pp.187-188
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• 2022

우리나라는 세계 최초로 유비쿼터스 도시(Ubiquitous City: U-City) 관련 법률을 제정하고 종합계획을 수립하는 등 스마트시티 관련 정책을 선도적으로 추진해 왔으며, 현재 스마트시티 정책은 스마트시티 국가시범도시 건설, 스마트 챌린지, 스마트도시형 도시재생사업 등 다양하게 추진 중이다. 본 연구에서는 최근 정보통신의 발달에 따른 디지털화 시대를 맞이하여 해외의 스마트정책 사례를 조사 및 분석하여 국내 도시문제 해결로 시민들의 삶의 질 향상을 위한 스마트시티를 구축할 수 있는 시사점을 도출할 예정이다.

• 스마트미디어저널
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• 제13권2호
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• pp.23-31
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• 2024

In precision cattle farm, reliably tracking the identity of each cattle is necessary. Effective tracking of cattle within farm environments presents a unique challenge, particularly with the need to minimize the occurrence of excessive tracking trajectories. To address this, we introduce a trajectory playback decision tree algorithm that reevaluates and cleans tracking results based on spatio-temporal relationships among trajectories. This approach considers trajectory as metadata, resulting in more realistic and accurate tracking outcomes. This algorithm showcases its robustness and capability through extensive comparisons with popular tracking models, consistently demonstrating the promotion of performance across various evaluation metrics that is HOTA, AssA, and IDF1 achieve 68.81%, 79.31%, and 84.81%.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.136-148
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• 2024

In recent years, public attention to health and wellness issues has increased. The integration of smart fitness hardware and innovative technologies have made the development of smart fitness a trend. The number of fitness applications in the market has surged, and demand for an optimal experience is increasingly high. This study selects Sweatcoin, Home Workout, Six Pack in 30 Days, and Fitness Coach & Diet as research subjects from the top ten global mobile health and fitness apps in 2022 based on download rankings. The research is based on eight gamification elements: motivation, challenge, achievement, relationships, sharing, reward, level, and competition, identified through preliminary studies. We distributed a total of 166 questionnaires to users and collected 163 valid responses for data analysis. The Kano Model was used to study the desires of fitness enthusiasts using fitness apps. To reduce the limitations of the research results, the Better-Worse Method was employed for satisfaction index analysis. Based on the final analysis, we propose suggestions for improvement for the four fitness apps to better meet user needs and create a more attractive and efficient application experience.

• 한국의류학회지
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• 제38권6호
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• pp.845-856
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• 2014

This study focused on the production and usability evaluation of smart outdoor jackets that are designed to provide convenience to middle-aged people by embedding devices for lighting and location tracing. The results were as follows. 1. Jacket power supplier was a assembled system composed of battery, charger, controller and switch. A solar cell was attached on the upper arm, and a wire type EL on the center line of a raglan sleeve along with a GPS on the left sleeve with a transparent vinyl pocket. The total weight of the jacket embedded with devices was 385-520g. 2. Operation of function, activity, acceptability, safety, convenience for device use, appearance, practical maintenance were selected based on an analysis of evaluation criteria of previous smart wear research. Criteria were narrowed to three major categories of satisfaction, appearance and maintenance. 3. Use satisfaction criterion consisted of wearable device functionality and physical, psychological use convenience. The evaluation indicated actual functionality. EL functions were especially effective and necessary. Convenience of use showed that a smart jacket was thought to be safe and the size was moderate regardless of age and gender. Outer appearance was satisfactory and respondents praised the color. The practical maintenance evaluation indicated that there was no challenge in doing the laundry since the solar battery and GPS were detachable. The practical use of smart outdoor jackets confirmed by fabric that was washable and dried quickly.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.2086-2098
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• 2018

Security plays a vital role and is the key challenge in Mobile Ad-hoc Networks (MANET). Infrastructure-less nature of MANET makes it arduous to envisage the genre of topology. Due to its inexhaustible access, information disseminated by roaming nodes to other nodes is susceptible to many hazardous attacks. Intrusion Detection and Prevention System (IDPS) is undoubtedly a defense structure to address threats in MANET. Many IDPS methods have been developed to ascertain the exceptional behavior in these networks. Key issue in such IDPS is lack of fast self-organized learning engine that facilitates comprehensive situation awareness for optimum decision making. Proposed "Intelligent Behavioral Hybridized Intrusion Detection and Prevention System (IBH_IDPS)" is built with computational intelligence to detect complex multistage attacks making the system robust and reliable. The System comprises of an Intelligent Client Agent and a Smart Server empowered with fuzzy inference rule-based service engine to ensure confidentiality and integrity of network. Distributed Intelligent Client Agents incorporated with centralized Smart Server makes it capable of analyzing and categorizing unethical incidents appropriately through unsupervised learning mechanism. Experimental analysis proves the proposed model is highly attack resistant, reliable and secure on devices and shows promising gains with assured delivery ratio, low end-to-end delay compared to existing approach.

• 스마트미디어저널
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• 제8권4호
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• pp.72-79
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• 2019

The automated demand response (DR) program encourages consumers to participate in grid operation by reducing power consumption or deferring electricity usage at peak time automatically. However, successful deployment of the automated DR program sphere needs careful assessment of appliances load profile (ALP). To this end, the recent method estimates frequency, consistency, and peak time consumption parameters of the daily ALP to compute their potential score to be involved in the DR event. Nonetheless, as the daily ALP is subject to varying with respect to the DR time ALP, the existing method could lead to an inappropriate estimation; in such a case, inappropriate appliances would be selected at the automated DR operation that effected a consumer comfort level. To address this challenge, we propose a more proper method, in which all the three parameters are calculated using ALP that overlaps with DR time, not the total daily profile. Furthermore, evaluation of our method using two public residential electricity consumption data sets, i.e., REDD and REFIT, shows that our energy management systems (EMS) could properly match a DR target. A more optimal selection of appliances for the DR event achieves a power consumption decreasing target with minimum comfort level reduction. We believe that our approach could prevent the loss of both utility and consumers. It helps the successful automated DR deployment by maintaining the consumers' willingness to participate in the program.

• International Journal of Internet, Broadcasting and Communication
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• 제15권2호
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• pp.144-156
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• 2023

Smart agriculture is a rapidly growing field that seeks to optimize crop yields and reduce risk through the use of advanced technology. A key challenge in this field is the need to create a comprehensive smart farm system that can effectively monitor and control the growth environment of crops, particularly when cultivating new varieties. This is where fuzzy theory comes in, enabling the collection and analysis of external environmental factors to generate a rule-based system that considers the specific needs of each crop variety. By doing so, the system can easily set the optimal growth environment, reducing trial and error and the user's risk burden. This is in contrast to existing systems where parameters need to be changed for each breed and various factors considered. Additionally, the type of house used affects the environmental control factors for crops, making it necessary to adapt the system accordingly. While developing such a framework requires a significant investment of labour and time, the benefits are numerous and can lead to increased productivity and profitability in the field of smart agriculture. We developed an AI platform for optimal control of facility houses by integrating data from mushroom crops and environmental factors, and analysing the correlation between optimal control conditions and yield. Our experiments demonstrated significant performance improvement compared to the existing system.