• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.631-632
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• 2008

In this paper, we propose service discovery mechanism, using CALM agent platform to guarantee scalability of scope of available services without modifying existing service discovery protocols. The proposed service discovery mechanism provides users with a wider selection of services, and convenient search methods.

• Journal of Distribution Science
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• v.16 no.7
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• pp.67-75
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• 2018

Purpose - In the 21st century, as information technology advances alongside the emergence of the 4th generation, industrial age, industrial environment has become individualized and customized. It is important to hire good quality employees for good service in the industry. The e-learning market is growing every year. Although e-learning companies are finding better quality employees in e-learning, it is not easy to find it. Companies also spend a lot of time and cost to find employee. On the employees side, they want to get a job freely when they want, but they cannot find their job easily. Furthermore, the labor market environment is changing fast. In the 4th generation, industrial age, employers require to find manpower whenever they need and want at little cost. So of their own accord, we have considered the necessity of management of human resources for employees and employers in e-learning. The purpose of this study is to propose a human cloud platform framework for enabling an efficient management of human resources in e-learning industry. Research design, data, and methodology - To pinpoint the items of a human cloud platform framework, the study was initiated according to the following process. First, items of competency relating to e-learning instructional designer was analyzed. Second, based on the items of information from this analysis, selection and validity verification took place with 5 e-learning specialists group. Third, the opinion of experts who were in charge of hiring in e-learning companies were collated with the questionnaire. Lastly, the human cloud platform framework was proposed based on opinion results. Results - The framework was comprised of 7 domains and 27 items in order to develop the human cloud platform for e-learning instructional designer. The analysis results showed that the most highly considered item were 'skill (4.60)' that employee already have the capability. Following this (in order) were 'project type (4.56)', 'work competency (4.56)', and 'strength area of instructional design (4.52)'. Conclusions - The 27 items in the human cloud platform framework were suggested in this study. Following this, we can consider to develop the human cloud platform for finding a job and hiring e-learning instructional designer easily. For successful platform operation, we need to consider reliability between employer and employee. In addition, we need quality assurance system based on operation has public confidence.

• Journal of Korean Dental Hygiene Science
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• v.5 no.2
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• pp.97-104
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• 2022

Background: Due to the declining number of students preparing for university entrance exams , the quota of universities has been decreasing continuously. This situation became increasingly diverse as new media used online, mobile, and PR tools to continuously invite students. This study is aimed at offering the helpful data to plan an effective PR strategy by analyzing the correlation between the major selection and satisfaction of the department's social media usage among freshmen majoring in dental hygiene. Methods: The collected data from the self-reported survey with freshmen were analyzed using the SPSS 22.0 program. The survey items were motive to select the major, social media platform that subjects used, reasons to use the media, time to visit the department's social media platform, and satisfaction level on the department's social media platform, using a 5-point Likert Scale. Results: The reasons for choosing a major were given by 32.2% and 15.9% respondents, respectively, as the vision after graduation and practice facilities. 39.9% and 31.4% used Instagram and YouTube for social media platforms, respectively, for using social media platform; 26.9% and 26.3% visited the department's social media before and after entering the university, respectively; 46.4% and 24.9% used Instagram and YouTube for department social media; and they generally satisfied with the contents of the department's social media. 40.9% of them said that information from the department's social media was useful. 33.8% of them said the information from the department's social media exceeded their expectations. 46.8% of them answered that the department's social media made the department's image positive. 33.4% got interested in the major more due to the department's social media. According to 32.1% of respondents, the department's social media was helpful in deciding on a major. With 35.4%, a positive correlation was discovered between the department's practice facilities and satisfaction on the department's social media. Conclusion: It is thought that the department's social media should try continuously by uploading the contents to meet the users' needs on a regular basis and seeking the plans to be able to collect various opinions using surveys through the related social media so that students can select the major and, moreover, lead the positive direction to adapt the university life under the unfamiliar environment after admission.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.4
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• pp.764-781
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• 2016

During the last two decades, industrial cluster policies for promoting regional economic growth and industrial development have been flourishing all over the world. Even though cluster policies have partly contributed to regional industrial growth and innovation capabilities, they have long been blamed for regional industrial lock-ins and declining regional industrial resilience because of applying homeogenous cluster policies and regional specialized strategic industrial promotion policies for various localities, which are based on so-called 'selection and concentration' principle. This paper suggests postcluster policy focused on placed-based smart specialization and regional business platform strategies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.738-745
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• 2014

Recently, mobile users continuously increase, and mobile applications also increase As mobile applications increase, the mobile users used to store sensitive and private information such as Bank information, location information, ID, password on their mobile devices. Therefore, recent malicious application targeted to mobile device instead of PC environment is increasing. In particular, since the Android is an open platform and includes security vulnerabilities, attackers prefer this environment. This paper analyzes the performance of malware detection system applying linear SVM machine learning classifier to detect Android malware application. This paper also performs feature selection in order to improve detection performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4658-4679
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• 2017

Android is now the most popular smartphone platform and remains rapid growth. There are huge number of sensitive privacy information stored in Android devices. Kinds of methods have been proposed to detect Android malicious applications and protect the privacy information. In this work, we focus on extracting the fine-grained features to maximize the information of Android malware detection, and selecting the least joint features to minimize the number of features. Firstly, permissions and APIs, not only from Android permissions and SDK APIs but also from the developer-defined permissions and third-party library APIs, are extracted as features from the decompiled source codes. Secondly, feature selection methods, including information gain (IG), regularization and particle swarm optimization (PSO) algorithms, are used to analyze and utilize the correlation between the features to eliminate the redundant data, reduce the feature dimension and mine the useful joint features. Furthermore, regularization and PSO are integrated to create a new joint feature mining method. Experiment results show that the joint feature mining method can utilize the advantages of regularization and PSO, and ensure good performance and efficiency for Android malware detection.

• Smart Structures and Systems
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• v.2 no.2
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• pp.189-208
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• 2006

Monitoring large-scale civil engineering structures such as offshore platforms and high-large buildings requires a large number of sensors of different types. Innovative sensor data information technologies are very extremely important for data transmission, storage and retrieval of large volume sensor data generated from large sensor networks. How to obtain the optimal sensor set and placement is more and more concerned by researchers in vibration-based SHM. In this paper, a method of determining the sensor location which aims to extract the dynamic parameter effectively is presented. The method selects the number and place of sensor being installed on or in structure by through the tolerance domain statistical inference algorithm combined with second order sensitivity technology. The method proposal first finds and determines the sub-set sensors from the theoretic measure point derived from analytical model by the statistical tolerance domain procedure under the principle of modal effective independence. The second step is to judge whether the sorted out measured point set has sensitive to the dynamic change of structure by utilizing second order characteristic value sensitivity analysis. A 76-high-building benchmark mode and an offshore platform structure sensor optimal selection are demonstrated and result shows that the method is available and feasible.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1530-1537
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• 2007

In this paper, we implemented a speech emotion recognition system that can distinguish human emotional states from recorded speech captured by a single microphone and classify them into four categories: neutrality, happiness, sadness and anger. In general, a speech recorded with a microphone contains background noises due to the speaker environment and the microphone characteristic, which can result in serious system performance degradation. In order to minimize the effect of these noises and to improve the system performance, a MA(Moving Average) filter with a relatively simple structure and low computational complexity was adopted. Then a SFS(Sequential Forward Selection) feature optimization method was implemented to further improve and stabilize the system performance. For speech emotion classification, a SVM pattern classifier is used. The experimental results indicate the emotional classification performance around 65% in the computer simulation and 62% on the ARM platform.

• Ocean Systems Engineering
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• v.9 no.2
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• pp.191-218
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• 2019

The tower-platform interface and mooring system of floating offshore wind turbines (FOWTs) are some of the most critical components with significant influences on overall project costs. In addition to satisfying strength requirements, it is typical and vital to meet fatigue criteria for a service life of 25 years or more. Wind spectra characteristics considered in analysis can penalize fatigue designs, leading to unnecessary costs. The International Electrotechnical Commission (IEC, 2009) recommends the use of site-specific wind data (spectrum, turbulence intensity, etc.) in design of FOWTs, but for offshore sites it is often the case that such data is unavailable and land-based data are used as surrogates in design. For such scenarios, it is worth investigating whether such alternative approach is suitable and accurate, and understanding the consequence of the selection of wind spectral characteristics on fatigue design. This paper addresses the impact of the subsequent selection on fatigue responses of towerbase and mooring system in a FOWT, as a sequel to the paper by Udoh and Zou (2018) which focused on impacts on strength design. The 5 MW semi-submersible FOWT platform with six mooring lines implemented in the preceding study is applied in analysis. Results indicate significant variations in resulting fatigue life with considered wind parameters. Thus, it is critical to apply proper wind spectra characteristics for analysis and design of FOWTs to avoid unnecessary conservatism and costs. Based on the findings of this study, more explicit guidance on the application of turbulence intensities for IEC-recommended models in offshore sites could lead to more accurate load estimates in design of FOWTs.

• Journal of Platform Technology
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• v.8 no.2
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• pp.10-21
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• 2020

The use of WiFi is gradually increasing through the spread of mobile terminals and the development of data communication. Mobile Internet usage has been steadily increasing from the 2000s to the present. Almost all households in Korea have smart devices, and 90% of the population uses mobile Internet. Due to this trend, the government is currently constructing public WiFi zones in dense urban areas as a way to reduce communication costs. The WiFi usage in the public WiFi zone is increasing every year. Therefore, in this paper, we propose a method for using such public WiFi efficiently. A mobile terminal collects WiFi information and constructs a WiFi zone in a map using a concave hull algorithm. In the map, the mobile terminal provides a route through many public WiFi areas. As a result, the WiFi usage of the mobile terminal is increased through more WiFi regions.