• Journal of the Korea Convergence Society
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• v.6 no.6
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• pp.185-193
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• 2015

The wisdom contents consists of an ordinary person's ideas and experience. The Wisdom Market [1] is an online business model where wisdom contents are traded. Thus, the general public could do business activities in the Wisdom Market at ease. As the wisdom contents are themselves the thought of persons, there exists many similar or duplicated contents. Existing copyright protection schemes mainly focus on the primary author's right. Thus, it's not appropriate for protecting the contents of Collective Intelligence that requires to protect the rights of collaborators. There should exist a new method to be dynamic capable of combining and deleting rights of select collaborators. In this study, we propose collective copyright authentication scheme suitable for the contents of Collective Intelligence. The proposed scheme consists of collective copyright registration, addition and verification protocols. It could be applied to various business models that require to combine multiple rights of similar contents or to represent multiple authorships on the same contents.

• Information Systems Review
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• v.8 no.3
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• pp.135-152
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• 2006

This study has been performed to analyze and identify some factors which has impact on the level of consumers' acceptance for the mobile ads. For better results and more reasonable generalization of the results, this study targets consumers in two such countries as Korea and U.S.A. This research investigates the determinants of consumer's acceptance for the mobile Ads. Based on TAM(Technology Acceptance Model) and Flow theory, our research adopts some factors for a theoretical model such as entertainment, information, irritation, credibility, flow experience, attitudes and acceptance intention. For examining an implied relationships by this research model, a survey was conducted by collecting research data from students and consumers of mobile ads in Korea, and U.S.A. This data was analyzed based on using AMOS, the structural equation modeling and a second-generation multi variant technique, and has gained distinct advantages over other technique.

• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.81-92
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• 2014

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.181-188
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• 2021

Research and importance of legal services applied with AI so that it can be easily understood and predictable in difficult legal fields is increasing. In this study, based on the decision of the Tax Tribunal in the field of tax law, a model was built through self-learning through information collection and data processing, and the prediction results were answered to the user's query and the accuracy was verified. The proposed model collects information on tax decisions and extracts useful data through web crawling, and generates word vectors by applying Word2Vec's Fast Text algorithm to the optimized output through NLP. 11,103 cases of information were collected and classified from 2017 to 2019, and verified with 70% accuracy. It can be useful in various legal systems and prior research to be more efficient application.

• Journal of Korean society of Dental Hygiene
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• v.23 no.4
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• pp.287-293
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• 2023

Objectives: In this study, dental hygienists' perceptions of dental hygienists' communication and general public's mutual perceptions were compared to seek development directions and plans for improving dental hygienists' communication skills, and furthermore, communication to increase satisfaction between the general public and dental hygienists. This study was conducted to provide basic data on the development and direction of competency improvement education. Methods: From April 1 to April 31, 2023, a survey was conducted through an online link for the general public with dental treatment experience and dental hygienists currently working at dentists in Gwangju and Jeonnam. A total of 258 questionnaires were collected. Independence t-test and paired t-test were performed using SPSS Statistics (ver. 21.0; IBM Corp., Armonk, NY, USA) Results: As a result of analyzing the accuracy of the communication ability of dental hygienists, from the dental hygienist's point of view, general publics perceived that the dental hygienist's underestimation of their own communication ability was actually lower than the dental hygienist's underestimated communication ability. From the general public's point of view, dental hygienists overestimated their communication ability. In fact, dental hygienists perceived their general publics as higher than general public's overestimated communication ability. Conclusions: Based on these results, it is necessary to develop education and operate various educational programs to improve the communication skills of dental hygienists, and to rethink educational accessibility to increase participation in education and to publicize the professionalism of dental hygienists.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.4
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• pp.408-420
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• 2023

As the cultural industry might be regarded as the most intimate industry to the general public, it is relatively easy to be widely accepted. With the development of the internet, not only people in various countries have been closely connected, but production networks around the world might also be connected with each other. This article will use data and case studies to clarify how global production networks operate in the development of the cultural industry. By taking the relatively novel point of contact of connection between global production networks and the development of the cultural industry, it summarizes the development models of the film, television and music sectors in the Korean cultural industries. The study found that the development model of the film, television and music industry from the 1990s to the present could be divided into four phases, and most firms are now in the outsourcing and expansion phase. Relying on the huge production networks, these two industries are likely to be improving their popularity and added value through global cooperation.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.137-143
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• 2014

Recently, as new paradigm for government operation called government 3.0, government is actively operating policy opening and sharing public data. In addition, the Ministry of Land are operating an open platform integrated map service (the VWorld) which provides a variety of video contents such as the country's national spatial information, traffic information and three-dimensional building for the public. According to W3C Foundation's Open Data Status Report(2013), our country has the evaluated results that the part of the government's policy support and planning is good while the part of the data management is vulnerable. So our country needs the quality improvement for the data management. In addition, a digital aerial photograph image data is required to be up-to-date for the three-dimensional spatial object data. In this paper, we present the method for enhancement of the accuracy of vertical position and for maintainment of up-to-date vertical position. Our methods evaluate the data quality and analyze the cause of error of measurement utilizing the national standard quality assessment method. The result of research shows that the accuracy of vertical position is improved if the height of the building captain is adjusted by the quality assessment values and a three-dimensional model has up-to-date data if reconstruction and extension information of construction register is utilized.

• Journal of Navigation and Port Research
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• v.38 no.5
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• pp.437-442
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• 2014

Ocean Literacy is an understanding of the ocean's influence on you and your influence on the ocean. This research developed the 7 ocean education model courses using ocean literacy based on the analysis of ocean education programs which executed 23 agencies in Busan. These model courses are combined in the type of indoor theory, indoor experience, field study and field experience. Also, this makes the guide map for ocean education in a 76cm*56cm size to distinguish and choose the course easily. This map is the format combined in geological location and tourist attraction spots in Busan, includes education centers, contents, lead time and so on, and it is possible for educatees to handle their preference and seasonality elastically. This map including ocean education model course is a milestone to activate ocean education, and is helpful to reach the goal of ocean education and to lead ocean professionals. In addition, this research presents the development of teaching materials, training aids to complement the weakness of indoor education, the development of cyber education through making video contents as the activation measures of ocean education.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.41-51
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• 2023

In this paper, we propose a technique of multi-time window feature extraction for anger detection in gait data. In the previous gait-based emotion recognition methods, the pedestrian's stride, time taken for one stride, walking speed, and forward tilt angles of the neck and thorax are calculated. Then, minimum, mean, and maximum values are calculated for the entire interval to use them as features. However, each feature does not always change uniformly over the entire interval but sometimes changes locally. Therefore, we propose a multi-time window feature extraction technique that can extract both global and local features, from long-term to short-term. In addition, we also propose an ensemble model that consists of multiple classifiers. Each classifier is trained with features extracted from different multi-time windows. To verify the effectiveness of the proposed feature extraction technique and ensemble model, a public three-dimensional gait dataset was used. The simulation results demonstrate that the proposed ensemble model achieves the best performance compared to machine learning models trained with existing feature extraction techniques for four performance evaluation metrics.