• 경영과학
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• 제13권2호
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• pp.73-94
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• 1996

Knapsack problems represent many business application such as cargo loading, project selection, and capital budgeting. In this research we developed a knapsack problem solver based on Martello-Toth algorithm using a relational database management system on the PC platform. The solver used the menu-driven user interface. The solver can be easily integrated with the database of decision support system because the solver can access the database to retrieve the data for the model and to store the result directly.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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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.

• 한국경제지리학회지
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• 제19권4호
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• pp.764-781
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• 2016

지난 20여 년간 지역경제성장과 산업발전을 위해서 다양한 분야에서 클러스터정책이 진행되었다. 클러스터정책은 지역의 산업진흥과 혁신역량강화에 어느 정도 기여했지만, 모든 지역에 동일한 처방의 적용, 선택과 집중이라는 특화산업 중심의 지역산업정책으로 인한 산업고착화와 지역회복력 쇠퇴, 업종중심의 산업정책의 문제 등을 노정하고 있다. 이 글에서는 산업의 융합화와 이에 따른 산업공간의 변화를 고찰하고, 산업지역의 혁신과 거버넌스의 변화의 추세를 분석한다. 기존의 지역특화 산업정책의 문제를 비판하고 지역산업의 발전을 위한 포스트 클러스터정책을, 장소기반의 스마트전문화 전략과 지역의 융복합 산업정책 중심으로 전환이 되어야 한다고 주장한다.

• 한국통신학회논문지
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• 제39C권8호
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• pp.738-745
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• 2014

최근 모바일 사용자들이 증가하면서 모바일 어플리케이션 또한 계속적으로 증가하고 있다. 모바일 어플리케이션이 증가하면서 사용자들은 모바일 장치에 은행정보, 위치정보, 아이디, 패스워드 등의 민감한 정보들을 저장하고 있다. 따라서 최근에는 PC를 타겟으로 하는 악의적인 어플리케이션보다 모바일 장치를 타겟으로 하는 악의적인 어플리케이션들이 증가하고 있는 추세이다. 특히 안드로이드 플랫폼의 경우 오픈 플랫폼으로써 사용자들에게 악성 코드를 포함한 어플리케이션을 배포하기 유리한 환경을 가지고 있다. 본 논문에서는 안드로이드 환경에서 악성코드를 포함한 어플리케이션을 탐지하기 위해 선형 SVM(Support Vector Machine) 기계학습 분류기를 적용한 악성코드 탐지 시스템의 성능을 분석한다. 또한 모바일 악성코드의 탐지 성능 향상을 위한 feature를 제시하고, 의미있는 feature를 선정한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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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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• 제2권2호
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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.

• 유통과학연구
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• 제16권7호
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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.

• 대한치위생과학회지
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• 제5권2호
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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.

• 한국멀티미디어학회논문지
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• 제10권11호
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• pp.1530-1537
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• 2007

본 논문은 마이크로폰을 통해 실시간으로 습득된 음성으로부터 사람의 음성 감성상태를 평상, 기쁨, 슬픔, 화남 등 4가지로 구별할 수 있는 ARM 플랫폼 기반의 음성 감성인식 시스템 구현에 관한 것이다. 일반적으로 마이크로폰으로 수신된 음성은 화자 주변의 환경 잡음과 마이크로폰의 시스템 특성 때문에 입력 음성 신호가 왜곡되고 이로 인해 시스템의 성능이 저하된다. 본 논문에서는 이러한 잡음 영향을 최소화하기 위해 비교적 단순한 구조와 적은 연산 량을 가진 이동평균(MA, Moving Average) 필터를 입력 음성의 특징벡터 열에 적용하였다. 또한, 효율적으로 감성 특징벡터를 최적화할 수 있는 SFS(Sequential Forward Selection)기법을 적용해 제안 시스템의 성능을 최적화하였으며 감성 패턴 분류기로는 SVM(Support Vector Machine)을 사용하였다. 실험 결과 제안 감성인식 시스템은 모의실험에서 약 65%, ARM 플랫폼에서 약 62%의 인식률을 보였다.

• Ocean Systems Engineering
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• 제9권2호
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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.