• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.203-210
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• 2011

The current sensor network analyzes the data collected by the sensing of fixed sensor nodes and provides a service. However, this method cannot actively handle the state and the change in the position of people, 'the target for sensing and the change in the environment', including home automation, building automation and real-time road & weather information, and healthcare environment, etc. To support a dynamic situation which is appropriate for an individual in this diverse environment, it is necessary to provide actively differentiated specific information according to the movement of people and the changes in the environment. In this study, a individualized sensor mobile agent middleware which provides the individualized information (the location of fire incidence and the trace for the path of spread), has been realized through the sensor network environment constructed by the installation of wireless sensor nodes mounted with mobile agent middlewares in buildings.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.21-31
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• 2018

In Hyper-Connected Society, IoT-based Lifelog data is used throughout the Internet and is an important component of customized services that reflect user requirements. Also, Users are using social network services to easily express their interests and feelings, and various life log data are being accumulated. In this paper, Intelligent characters using IoT based lifelog data have been developed and qualitative/quantitative data are collected and analyzed in order to systematically grasp emotions of users. For this, qualitative data through the social network service used by the user and quantitative data through the wearable device are collected. The collected data is verified for reliability by comparison with the persona through esnography. In the future, more intelligent characters will be developed to collect more user life log data to ensure data reliability and reduce errors in the analysis process to provide personalized services.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.229-235
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• 2015

ICT healing platform is based on bio-signal and life habit information target to alarm early sickness concept prevention chronic pain. ICT(Information & Communication Technology) healing platform target on personal lead health management care of several health agencies and open of the (hospital, fitness center, health examination center, personal health device) personal health information together to personal device. Support Analysis Platform and Open API to vitalization optional services. In this paper proposal to access personality healing data Open Gateway Framework of Healing Platform Adaptor (HPAdaptor) ICT healing platform means Data relaying link to EMR(Electronic health record), korean medicine, life log, wellness, chronic pain, and fineness several personal health data provider and service provider personal healing data with software engine. After Design HPAdaptor can use for data and service provider record storage, mobile platform and analytics platform need data service or platform relying reference model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.149-162
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• 2021

This paper established a new smart factory based on manufacturing data for an introductory company focusing on the personalized cosmetics manufacturing industry. We build on an example of a system that collects, manages, and analyzes documents and data that were previously managed by CGMP-based analog for data-driven use. To this end, we have established a system that can collect all data in real time at the production site by introducing artificial intelligence smart factory platform LINK5 MOS and POP system, collecting PLC data, and introducing monitoring system and pin board. It also aims to create a new business cluster space based on this project.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.193-198
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• 2011

As smartphone use has been steadily growing over the past year, social networking on the mobile phones appears to be increasing. In this paper, we propose a cross-platform mobile architecture for supporting social applications. The key design objectives for developing the proposed system include: 1) providing personalized data through filtering information which is supplied by various social applications, 2) providing a cross-platform architecture for adapting various smartphones such as Apple iPhone and Google android. We verified system design by implementing a smartphone application which displays the filtered pictures from social network services such as Flickr and Picasa.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.1-7
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• 2020

As the demand for health screening increases, there is a need for efficient design of screening items. We build machine learning models for health screening and recommend screening items to provide personalized health care service. When offline, a synthetic data set is generated based on guidelines and clinical results from institutions, and a machine learning model for each screening item is generated. When online, the recommendation server provides a recommendation list of screening items in real time using the customer's health condition and machine learning models. As a result of the performance analysis, the accuracy of the learning model was close to 100%, and server response time was less than 1 second to serve 1,000 users simultaneously. This paper provides an adaptive and automatic recommendation in response to changes in the new screening environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.223-228
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• 2023

According to the Korea Information Society Development Institute, OTT services grew at a rate of 33.4% in four yearsfrom 2017, when they were first launched.TheKorea Export-Import Bank announced in 2020 that the domestic OTT market was worth 780.1 billionKRW. This growth of the OTT market is expected to stimulate competition among OTT service platforms, and user satisfactionwithconvenience features, such as video recommendations, seems to be acting as an important factor in the competition.Currently, the OTT market uses a variety ofdata for customized recommendations, but the limitationis that it only uses datacollected within the app. Thereby we have proposed the use ofpersonal data collected outside the app for personalized recommendations, and the survey results showed that user satisfaction was 23.72% higher for recommended content based on the proposedmethod thanNetflix recommended content.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1149-1158
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• 2004

Today the fastest growing communities of web users are mobile visitors who browse web page with wireless PDAs and cellular phones. However, most web pages are optimiaed exclusively for desktop clients on the broadband network and are inconvenient to users with small screen mobile devices. They display only a few lines of text and cannot run client-side programs or scripts due to lack of system resource. Even worse, their connections are usually slow to support most of the data-intensive applications. In this paper, we propose a pageslet scheme that makes it feasible to browse ordinary web pages on small screen mobile devices. It extracts broadcasting sections of user preference from broadcasting web pages and automatically reorganizes the extracted sections for convenient browsing on mobile devices.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.159-166
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• 2023

Due to the aging workforce in the construction industry in South Korea, the accident rate has been increasing. The cognitive abilities of older workers are closely related to both safety incidents and labor productivity. Therefore, there is a need to improve cognitive abilities through personalized training based on cognitive assessment results, using cognitive training content, in order to enable safe performance in labor-intensive environments. The provided cognitive training content includes concentration, memory, oreintation, attention, and executive functions. Difficulty levels were applied to each content to enhance user engagement and interest. To stimulate interest and encourage active participation of the participants, the difficulty level was automatically adjusted based on feedback from the MMSE-DS results and content measurement data. Based on the accumulated data, individual training scenarios have been set differently to intensively improve insufficient cognitive skills, and cognitive training programs will be developed to reduce safety accidents at construction sites through measured data and research. Through such simple cognitive training, it is expected that the reduction of accidents in the aging construction workforce can lead to a decrease in the social costs associated with prolonged construction periods caused by accidents.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.95-117
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• 2022

With the emergence of various media types due to the development of Internet technology, advertisers have difficulty choosing media suitable for corporate advertising strategies. There are challenging to effectively reflect consumer contextual information when advertising media is selected based on traditional marketing strategies. Thus, a recommender system is needed to analyze consumers' past data and provide advertisers with personalized media based on the information consumers needs. Since the traditional recommender system provides recommendation services based on quantitative preference information, there is difficult to reflect various contextual information. This study proposes a methodology that uses deep learning to recommend personalized media to advertisers using consumer contextual information such as consumers' media viewing time, residence area, age, and gender. This study builds a recommender system using media & consumer research data provided by the Korea Broadcasting Advertising Promotion Corporation. Additionally, we evaluate the recommendation performance compared with several benchmark models. As a result of the experiment, we confirmed that the recommendation model reflecting the consumer's contextual information showed higher accuracy than the benchmark model. We expect to contribute to helping advertisers make effective decisions when selecting customized media based on various contextual information of consumers.