• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4626-4631
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• 2011

In this paper, we design an ubiquitous-type shopping mall using RFID(Radio Frequency IDentification) technology and a location analysis system which can locate customers in real time at off-line shopping malls as a way to provide any services to customers, based on the results of environmental analysis data. We also suggest an intelligent agent system which can provide a personalized information in time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.174-190
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• 2010

In ubiquitous computing, invisible devices and software are connected to one another to provide convenient services to users [1][2]. Users hope to obtain a personalized service which is composed of customized devices among sharable devices in a ubiquitous smart space (which is called USS in this paper). However, the situations of each user are different and user preferences also are various. Although users request the same service in the same USS, the most suitable devices for composing the service are different for each user. For these user requirements, this paper proposes a device recommender system which infers and recommends customized devices for composing a user required service. The objective of this paper is the development of the systems for recommending devices through context-aware inference in peer-to-peer environments. For this goal, this paper considers the context and user preference. Also I implement a prototype system and test performance on the real ubiquitous mobile object (UMO).

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.155-165
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• 2006

Along with the evolution of wireless internet environment, information services in mobile internet are being explosively increased. However, due to the high expense for the use of those services, it is not easy to collect the devoted customers. In order to overcome the adverse conditions, the service should be supported by a strategic application system which facilitates easier and fast service for customer's information needs than that of traditional on-line internet. In this paper, we investigate the strategy how to provide the information service under the customer satisfaction, and also consider some ways of personalization of information service and information provision in real-time. As a result of the works, we develop a system, MoSA-CRM(Mobile Service Application based on CRM) to support customer-oriented information service. We believe that the MoSA-CRM system can provide some benefits for information providers in mobile e-commerce market such as flexibility in business strategy, systematic customer relationship management, and growth in financial ability.

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.93-104
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• 2015

In this paper, the design of intelligent_advertisement_operation system(IAdOS) based on user's feedback is proposed for mobile environments. The proposed system stores the advertising contents created by the advertising provider and recommends the personalized advertising contents by analyzing the context information, and then feedback information of the advertisements. Since the proposed system which can recommends provide the smart advertisement contents based on personal preference, it is expected to contribute the new service model development of in the field of advertising market.

• Korean Journal of Radiology
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• v.22 no.9
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• pp.1569-1578
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• 2021

The Korean Thyroid Imaging Reporting and Data System (K-TIRADS) is an ultrasound-based risk stratification system for thyroid nodules that has been widely applied for the diagnosis and management of thyroid nodules since 2016. This review article provides an overview of the use of the K-TIRADS compared with other risk stratification systems. Moreover, this review describes the challenges in the clinical application of the K-TIRADS, as well as future development directions toward the personalized management of patients with thyroid nodules.

• Journal of the Korean Society for information Management
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• v.22 no.4 s.58
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• pp.197-220
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• 2005

This research aims to explore faculty research support systems in university libraries, assess their service performance for faculty research, and suggest the need and roadmap for new service development. We perform in-depth analyses of not only the current university library evaluation criteria as part of the overall university evaluation criteria, but also ten university libraries in the United States and two university libraries in Korea. Thorough benchmarking studies reveal the problems of the current university library evaluation criteria in its advances and limitations of current faculty support service systems. Especially this research suggests to develop a one-stop service execution wheel for the roadmap for the faculty research support system which is based on customer relationship management(CRM) for one-to-one, mass-personalized services to the faculty.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.7
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• pp.111-118
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• 2012

Recently, most of firms have continuously released new products satisfying various needs of customers in order to increase market share. As a lot of products with various functionalities, prices and designs are released in the market, users have difficulties in choosing an appropriate product, especially for information technology driven devices. In case of digital cameras, inexperienced users spend a lot of time and efforts to find proper model for them. In this study, therefore, we design and implement a personalized recommendation system using analytic hierarchy process, one of the multi-criteria decision making techniques, and hybrid filtering combining content-based filtering and collaborative filtering to recommend a suitable product for inexperienced users of information technology devices.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.4
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• pp.67-75
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• 2012

For the last few years, LBS has attracted considerable attention from many industries and societies as a result of propagated smart devices. LBS has a high utilization of mobile users as it uses user positions as a significant factor. Current LBS has only taken user position into account and it makes some limits. So, it is necessarily suggested that support for personalized services which consider user's motion, traffic condition, weather condition, time, personal information and preferences that have a huge impact on the accuracy. The purpose of this study is to design the inference systems with user's motion, preferences and schedules and provide users with the personalized information. To achieve this, Movement Ontology, User Profile Ontology, Schedule Ontology and Work Ontology should be constructed and based on this, smart applications were developed. Developed applications induced appropriately recommended results according to user's preference, motion and directions.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.293-301
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• 2017

Most new products not only suddenly disappear in the market but also quickly cannibalize older products. Under such a circumstance, retailers may have too much stock, and customers may be faced with difficulties discovering products suitable to their preferences among short life cycle products. To address these problems, recommender systems are good solutions. However, most previous recommender systems had difficulty in reflecting changes in customer preferences because the systems employ static customer preferences. In this paper, we propose a recommendation methodology that considers dynamic customer preferences. The proposed methodology consists of dynamic customer profile creation, neighborhood formation, and recommendation list generation. For the experiments, we employ a mobile image transaction dataset that has a short product life cycle. Our experimental results demonstrate that the proposed methodology has a higher quality of recommendation than a typical collaborative filtering-based system. From these results, we conclude that the proposed methodology is effective under conditions where most new products have short life cycles. The proposed methodology need to be verified in the physical environment at a future time.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.494-500
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• 2008

In the field of development of human interface technology, the interactions between human and machine are important. The research on emotion recognition helps these interactions. This paper presents an algorithm for emotion recognition based on personalized speech signals. The proposed approach is trying to extract the characteristic of speech signal for emotion recognition using PLP (perceptual linear prediction) analysis. The PLP analysis technique was originally designed to suppress speaker dependent components in features used for automatic speech recognition, but later experiments demonstrated the efficiency of their use for speaker recognition tasks. So this paper proposed an algorithm that can easily evaluate the personal emotion from speech signals in real time using personalized emotion patterns that are made by PLP analysis. The experimental results show that the maximum recognition rate for the speaker dependant system is above 90%, whereas the average recognition rate is 75%. The proposed system has a simple structure and but efficient to be used in real time.