• Child Studies in Asia-Pacific Contexts
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• v.2 no.1
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• pp.29-43
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• 2012

The purpose of the study was to generate the candidate items for the Adolescent Friendship Social Capital Scale. Both inductive and deductive approaches were used to generate the scale items. Halpern's conceptual map of social capital served as the theoretical basis of this scale, and guided the development of items. Semi-structured interviews with adolescents in Sydney, Melbourne and Beijing generated the initial pool of scale items. Twenty-six items were generated for the Adolescent Friendship Social Capital Scale. The items are organised in four theoretical constructs: Bonding Networks, Bridging Norms, Bridging Sanctions, and Linking Networks. Each item is a short statement followed by a five-point Likert scale anchored by 1= "Strongly disagree" and 5= "Strongly agree". The scale has several advantages over previous measures of adolescent friendship networks and friendship social capital. The scale has a strong and clear theoretical structure, the scale items demonstrate initial construct and content validity, and the format of the scale enables the collection of continuous data. However, in order to ensure the validity and reliability of the scale, another two stages of research need to be conducted in the future: scale development and scale evaluation.

• Korean Journal of Computational Design and Engineering
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• v.10 no.6
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• pp.412-420
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• 2005

The integration of CAD and CAM is critical for computer aided process planning. Recently, STEP has been emerged and utilized as a product data exchange standard format in CAD/CAM areas. The assembly information consists of assembly component relations, assembly features, assembly directions and mating conditions. The purpose of this study is to develop an efficient algorithm to extract assembly information directly from the STEP based files and to generate automatically assembly process plan from the extracted assembly features. The developed algorithms will generate assembly information codes for STEP(AICS). The results from this study can be a reasonable link between design and manufacturing for developing better CAAPP(Computer-Aided Assembly Process Planning) systems.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.11a
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• pp.28-33
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• 2000

This article describes the application of discrete event simulation in a process industry (coffee manufacturing) as a daily production-scheduling tool. A large number of end products (around 300), sporadic demand, and limited shelf life of coffee (90 days) make it difficult to generate feasible production schedules manually. To solve this problem an integrated system was developed incorporating discrete event simulation methodology into scheduling process. The integrated system is comprised of two components: a scheduling program and a simulation model. The scheduling program is used to generate daily schedules for roasting, grinding, and packing coffee. The simulation model uses the generated schedules to simulate the production of coffee and regenerates a modified production schedule. In this paper, each of the components will be described in detail, evaluated in terms of performance factors, and validated with a set of real production data. Although this article focuses on a specific system, we will share our experiences and intuitions gained and encourage other process industries to develop simulation-based scheduling tools.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.827-830
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• 2000

In this paper, we propose the method using PCA(principal component analysis) algorithm when proposed algorithm performs multimedia information indexing. After we extract DC coefficients of DCT from MPEG video stream which is an international standard of moving picture compression coding, we apply PCA algorithm to image made of DC coefficients and extract the feature of each DC image. Using extracted features, we generate codebook and perform multimedia information indexing. The proposed algorithm Is very fast when indexing and can generate optimized codebook because of using statistical feature of data

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.415-424
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• 2005

The rapid growth of blog has caused information overload where bloggers in the virtual community space are no longer able to effectively choose the blogs they are exposed to. Recommender systems have been widely advocated as a way of coping with the problem of information overload in e-business environment. Collaborative Filtering (CF) is the most successful recommendation method to date and used in many of the recommender systems. Therefore, we propose a CF-based recommender system for bloggers in the virtual community space. Our proposed methodology consists of three main phases: In the first phase, we apply the "Interest Value" to a recommender system. The Interest Value is a quantity value about user preference in virtual community, and can measure the opinion of users accurately. Next phase, we generate the neighborhood group based on the Interest Value. In the final phase, we use the Community Likeness Score (CLS) to generate the top-n recommendation list. The methodology is explained step by step with an illustrative example and is verified with real data of a blog service provider.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.337-339
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• 2003

This paper describes the SAR geocoding module, which is the sub-module of a IRHIS ('Integrated RS s/w for High resolution satellite ImageS'): package of 'Development of High Resolution Satellite Image Processing Technique' project in Electronics and Telecommunications Research Institute (ETRI). The function of this module is following. 1) Orbit Type : ERS1/ERS2, RADARSAT 2) Data Format : SAR CEOS Format(Single Look Complex) 3) Function: - Geocode : Generate a map projected SAR image based on only orbit information - Orthorectify: Generate a rigorous geocoded SAR image with a DEM information In this paper, we briefly describe the algorithm that is adopted to the functions, and component architecture.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.21-22
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• 2007

Topology processing is indispensable basic function as it generate a real-time BUS-BRANCH model in Energy Management Systems because most application softwares such as state estimation, power flow, etc., require BUS-BRANCH circuit data. This paper propose an expert system to generate BUS-BRANCH circuit model using Artificial Intelligence technology and it is applied to 154kV distribution substations.

• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.297-305
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• 2001

Ocean satellite altimetry-implied free-air gravity anomalies have had the shortest wavelengths removed during the processing to generate the optimal solution between multiple radar altimeter missions. ERS-1 168day mission altimetry was residualized to a reference geoid surface generated by integrating Anderson & Knudsen’s free-air gravity anomalies for the Barents Sea. The altimetry tracks were reduced and filtered to extract the shortest wavelengths (between 4 and 111 km) from both ascending and descending tracks, respectively. These data were recombined using existing quadrant-swapping techniques in the wavenumber domain to generate a correlated, high frequency gravity field related to the local geologic sources. This added-value surface adjusted the reference free-air gravity anomalies to better reflect features in the gravity field at a wavelength related to the distance between altimetry ground tracks.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4060-4066
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• 2021

Flow-accelerated corrosion (FAC) of carbon steel piping is a significant problem in nuclear power plants. The basic process of FAC is currently understood relatively well; however, the accuracy of prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose a methodology to construct pipe wall-thinning rate prediction models using artificial neural networks and a convolutional neural network, which is confined to a straight pipe without geometric changes. Furthermore, a methodology to generate training data is proposed to efficiently train the neural network for the development of a machine learning-based FAC prediction model. Consequently, it is concluded that machine learning can be used to construct pipe wall thinning rate prediction models and optimize the number of training datasets for training the machine learning algorithm. The proposed methodology can be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rate prediction model for a real situation.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.4
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• pp.149-154
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• 2018

Risk Based Oversight is a way of performing oversight, where planning is driven by the combination of risk profile and safety performance. Risk Based Oversight enables prioritization and allocation of State's safety management resources commensurate with the safety risk profile of each service provider. This paper presents the concept of Risk Based Oversight and applies it to the current Korean aviation safety oversight process. In particular, this study presents a method for generating operating profiles, one of the key concepts of Risk Based Oversight. Operations Specifications of Part 121 Airlines can generate operating profiles. In this study, a practical study was conducted to generate the operating profile and scoped DCTs using Part 121 Airlines's Operations Specifications.