• Journal of Digital Convergence
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• v.11 no.7
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• pp.185-192
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• 2013

The purpose of this study is to set the relationship between each parts that forms the engine of an automobile based on the 3D visualization technology which is able to be learned according to the skill of the operator in the industry field and to recommend the auto parts using a task ontology. A visualization method was proposed by structuring the complex knowledge by signifying the link and the node in forms of a network and using SOM which can be shown in the form of 3 dimension. In addition, by using is-a Relationship-based hierarchical Taxonomy setting the relationship between each of the parts that forms the engine of an automobile, to allow a recommendation using a weighted value possible. By providing and placing the complex knowledge in the 3D space to the user for an opportunity of more realistic and intuitive navigation, when randomly selecting the automobile parts, it allows the recommendation of the parts having a close relationship with the corresponding parts for easy assembly and to know the importance of usage for the automobile parts without any special expertise.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.337-342
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• 2010

The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users are tend to build play-list for manage songs. However the manual selection of songs for creating play-list is bothersome task. This paper proposes an auto play-list recommendation system considering user's context of use and preference. This system has two separate systems: mood and emotion classification system and music recommendation system. Users need to choose just one seed song for reflection their context of use and preference. The system recommends songs before the current song ends in order to fill up user play-list. User also can remove unsatisfied songs from recommended song list to adapt user preferences of the system for the next recommendation precess. The generated play-lists show well defined mood and emotion of music and provide songs that user preferences are reflected.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.2137-2139
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• 2013

Breast cancer is the most common cancer among females, worldwide, accounting for 22.9% of all cancers (excluding non-melanoma skin cancer) in females. Mammography is a sensitive (77-95%) and specific (94-97%) screening method for breast cancer. Previously, females between the 40-50 years old were recommended to have mammograms every one to two years. However, based on current evidence, in 2009, USPSTF recommended that the decision to start regular, biennial screening mammography for females younger than 50 years should be an individual decision and take patient context into account, including the patient's values regarding specific benefits and harms. This decision was based on findings regarding radiation exposure, false-positive and false-negative rates, over-diagnosis, and pain and psychological responses. The goal of this paper is to focus on evidence for updating the U.S. Preventive Services Task Force (USPSTF) recommendation against routine mammography for females between 40-49 years of age.

• Kidney Research and Clinical Practice
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• v.36 no.2
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• pp.111-116
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• 2017

The Korean Society of Nephrology participated in the task force team consisting of government authorities and civilian experts to prevent and control the spread of Middle East respiratory syndrome (MERS) in 2015. The Korean Society of Nephrology MERS Task Force Team took an immediate action and drafted 'the clinical recommendation for hemodialysis facilities' to follow when the first and the only confirmed case was reported in the hemodialysis unit. Owing to the dedicated support from medical doctors, dialysis nurses, and related medical companies, we could prevent further transmission of MERS infection successfully in hemodialysis units. This special report describes the experience of infection control during MERS outbreak in 2015 and summarizes the contents of 'the clinical practice guideline for hemodialysis facilities dealing with MERS patients' built upon our previous experience.

• Journal of agricultural medicine and community health
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• v.29 no.1
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• pp.15-28
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• 2004

Objectives: This study was performed to examined the effectiveness evaluation of peer education program on smoking prevention and cessation for elementary school students. Methods: Data were collected from 60 students in a rural area through self-administrated questionnaires. Child-leaders participated the peer education program to assist their friend, parent, and adult in community to quit the smoking for 4 weeks. Results and Conclusions: Major conclusions were as follows : 1. The peer education program on smoking prevention and cessation for elementary school students was reinforce to increasing the tobacco knowledge and the cessation skill, learning the communication skill, and improving the empowerment. 2. Image of tobacco, intention of smoking in future, recommendation for smoking cessation, pro of smoking. con of smoking, and level of assert in post-test were higher than those in pre-test. 3. There were significant differences in image of tobacco, con of smoking, and level of assert by grade between the pre-test and the post-test of peer education program. But intention of smoking in future, recommendation for smoking cessation, and pro of smoking were not related to effectiveness of peer education program. 4. Child-leaders for smoking prevention and cessation performed the their task to 1.4 persons per student. 5. Participating students were satisfied with the contents of program, the usefulness of educational materials, and preference of parents, but they were not satisfied with the usefulness of task note, learning time, and lecture room.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.7B
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• pp.619-631
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• 2006

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of context information, such as the user's location, to offer greater services to the user without any explicit request. In this paper, we propose context-aware active services on the basis of CAMUS (Context-Aware Middleware for URC Systems). CAMUS is a middleware for providing context-aware applications with development and execution methodology. Accordingly, the applications developed by CAMUS respond in a timely fashion to contexts. To evaluate, we apply proposed active services to TV application domain. Therefore, we implement and experiment the TV contents recommendation service agent, control service agent and TV task based on CAMUS. The context-aware TV task is to recommend programs and control of TV according to user preference, location and voice commands.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3192-3212
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• 2016

With the advent and popularity of e-commerce, an increasing number of consumers prefer to order tourism products online. A recommender system can help these users contend with information overload; however, such a system is affected by the cold start problem. Online tourism destination searching is a more difficult task than others on account of its more restrictive factors. In this paper, we therefore propose a tourism destination recommender system that employs opinion-mining technology to refine user preferences and item opinion reputations. These elements are then fused into a hybrid collaborative filtering method by combining user- and item-based collaborative filtering approaches. Meanwhile, we embed an artificial interactive module in our recommender system to alleviate the cold start problem. Compared with several well-known cold start recommendation approaches, our method provides improved recommendation accuracy and quality. A series of experimental evaluations using a publicly available dataset demonstrate that the proposed recommender system outperforms existing recommender systems in addressing the cold start problem.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1281-1284
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• 2015

Multi-day trip itinerary planning is complex and time consuming task, from selecting a list of worth visiting POIs to arranging them into an itinerary with various constraints and requirements. In this paper, we present CYTRIP, a multi-day trip itinerary planning system that engages human computation (i.e. crowd recommendation) to collaboratively recommend POIs by providing a shared workspace. CYTRIP takes input the collective intelligence of crowd (i.e. recommended POIs) to build a multi-day trip itinerary taking into account user's preferences, various time constraints and locations. Furthermore, we explain how we engage crowd in our system. The planning problem and domain are formulated as AI planning using PDDL3. The preliminary empirical experiments show that our domain formulation is applicable to both single-day and multi-day trip planning.

• Korean Journal of Radiology
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• v.24 no.1
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• pp.22-30
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• 2023

Ultrasonography (US) is a primary imaging modality for diagnosing nodular thyroid disease and has an essential role in identifying the most appropriate management strategy for patients with nodular thyroid disease. Standardized imaging techniques and reporting formats for thyroid US are necessary. For this purpose, the Korean Society of Thyroid Radiology (KSThR) organized a task force in June 2021 and developed recommendations for standardized imaging technique and reporting format, based on the 2021 KSThR consensus statement and recommendations for US-based diagnosis and management of thyroid nodules. The goal was to achieve an expert consensus applicable to clinical practice.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.117-125
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• 2022

Recently, as users' interest for travel increases, research on a travel route recommendation service that replaces the cumbersome task of planning a travel itinerary with automatic scheduling has been actively conducted. The most important and common goal of the itinerary recommendations is to provide the shortest route including popular tour spots near the travel destination. A number of existing studies focused on providing personalized travel schedules, where there was a problem that a survey was required when there were no travel route histories or SNS reviews of users. In addition, implementation issues that need to be considered when calculating the shortest path were not clearly pointed out. Regarding this, this paper presents a quantified method to find out popular tourist destinations using social big data, and discusses problems that may occur when applying the shortest path algorithm and a heuristic algorithm to solve it. To verify the proposed method, 63,000 places information was collected from the Gyeongnam province and big data analysis was performed for the places, and it was confirmed through experiments that the proposed heuristic scheduling algorithm can provide a timely response over the real data.