• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.351-356
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• 2006

Based on current practical needs for geo-spatial information on mobile platform, the main theme of this study is a design and implementation of dynamic 3D terrain rendering system using spaceborne imagery, as a kind of texture image for photo-realistic 3D scene generation on mobile environment. Image processing and 3D graphic techniques and algorithms, such as TIN-based vertex generation with regular spacing elevation data for generating 3D terrain surface, image tiling and image-vertex texturing in order to resolve limited resource of mobile devices, were applied and implemented by using graphic pipeline of OpenGL|ES (Embedded System) API. Through this implementation and its tested results with actual data sets of DEM and satellite imagery, we demonstrated the realizable possibility and adaptation of complex typed and large sized 3D geo-spatial information in mobile devices. This prototype system can be used to mobile 3D applications with DEM and satellite imagery in near future.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.2005-2013
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• 2008

Urban transit is a large scaled complex system which combines rolling stocks, power supply, signal communications, tracks & stations etc. KRRI develops nine key devices since July, 2007 as a part of the second phase of project on the standardization of urban rail transit system, which include information-communication system, station facilities, AC-DC current electric power system in urban transit. We promote the project under two directions, i.e. user-customer oriented standardization and strategic standardization for leading technologies in urban transit. In this paper, we present development plan of these key systems in view of system life cycle based on system engineering standards KSX ISO/IEC 15288 which supplies the common fundamental frame to describe the life cycle of artificial systems. System engineering process of KSX ISO/IEC 15288 are helpful to efficiently develop those key devices, although it is difficult to apply the standard identically to the key devices with the varieties and characteristics.

• Journal of Smart Tourism
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• v.1 no.2
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• pp.13-18
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• 2021

Hotels have recently started to implement enterprise information systems to measure and report sustainability indicators in a smart manner. However, a complex ownership structure in a hotel chain prevents full smart systems adoption at the individual property level. This study explores how a smart sustainability performance measurement system (SPMS) for waste management adoption correlates with customer ratings, customer reviews, operational efficiency scores, and between franchised and corporate-managed properties. We derive insights from the secondary data constructed from multiple sources for a large multinational hotel chain hotel. The findings suggest that hotels that adopt SPMS have better operational efficiency scores and more customer reviews. Within the hotels that adopted SPMS, corporate-managed hotels have a lower level of ratings than franchised hotels, but they have higher operational efficiency scores and more reviews. We discuss research implications for the concept of smart tourism and hotel management literature and managerial implications.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.192-198
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• 2022

Companies are building and utilizing their own data analysis systems according to business characteristics in the distributed cloud. However, as businesses and data types become more complex and diverse, the demand for more efficient analytics has increased. In response to these demands, in this paper, we propose an unsupervised learning-based data analysis agent to which reinforcement learning is applied for effective data analysis. The proposal agent consists of reinforcement learning processing manager and unsupervised learning manager modules. These two modules configure an agent with k-means clustering on multiple nodes and then perform distributed training on multiple data sets. This enables data analysis in a relatively short time compared to conventional systems that perform analysis of large-scale data in one batch.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.135-139
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• 2022

The current-voltage modeling plays an important role in characterizing photovoltaic systems. A solar cell has a nonlinear characteristic with various parameters influenced by the external environments such as the irradiance and the temperature. In order to accurately predict current-voltage characteristics at low irradiance, the artificial neural networks are applied to effectively quantify nonlinear behaviors. In this paper, a multi-layer perceptron scheme that can make accurate predictions is employed to learn complex formulas for large amounts of continuous data. The simulated results of artificial neural networks model show the accuracy improvement by using MATLAB/Simulink.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• The Korean Journal of Zoology
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• v.36 no.2
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• pp.285-292
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• 1993

This investigation has been carried out to clarify structural architecture of cerebral neuroendocrine systems in the fifth instar lanra of cabbage butterfly Pieris rapae. In order to examine the cerebral neurosecretorv cell systems the brain and retrocerebral neuroendocrine complex were histochemically stained with the paraldehvde fuchsin. The brain of the fifth instar laMa contains three kinds of neurosecretorv cells: medial, lateral and tritocerebral neurosecretorv cells. The axon bundles of medial and lateral neurosecretory cells form medial neurosecretory pathway(MNSP) and lateral neurosecretorv pathwav(LNSP) within the brain respectively. Especially, prior to exiting the brain, the axon bundles of medial neurosecretorH cells located in both left and right cefebral hemispheres decussate in cerebral medial region and project to contralateral retrocerebral neuroendocrine complexes. Outside the brain the axon bundles of medial and lateral neurosecretory cells form the nenri corporis cardiaca(NCC) I and II respectively. The NCC I and ll run together to the retrocerebral neuroendocrine complex, forming the large nenre bundles in both left md right sides. The anon bundles of tritocerebral neurosecretory cells which pass through the brain along the tritocerebral neurosecretory pathway (TNSP) form the Ncc III outids the train. some of the Ncc I and it terminate in the corpus cardiacum, while the others pass through the corpus cardiacum without termination. The nerve bundle which passes the corpus cardiacum forms the nenrus corforis allatum(NCA) I which runs between the corpus cardiacum and the corpus allatum. Theyt are finally innervated to the corpus allatum. The Ncc III Projects to the corpus cardiRcum. However, most of NCC III priss through the corpus cardiacum without branching and then run down for another organ.

• Computers and Concrete
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• v.22 no.2
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• pp.227-237
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• 2018

Today, the modeling of concrete as a material within finite element simulations is predominantly done through nonlinear material models of concrete. In current sophisticated computational systems, there are a number of complex concrete material models which are based on theory of plasticity, damage mechanics, linear or nonlinear fracture mechanics or combinations of those theories. These models often include very complex constitutive relations which are suitable for the modeling of practically any continuum mechanics tasks. However, the usability of these models is very often limited by their parameters, whose values must be defined for the proper realization of appropriate constitutive relations. Determination of the material parameter values is very complicated in most material models. This is mainly due to the non-physical nature of most parameters, and also the large number of them that are frequently involved. In such cases, the designer cannot make practical use of the models without having to employ the complex inverse parameter identification process. In continuum mechanics, however, there are also constitutive relations that require the definition of a relatively small number of parameters which are predominantly of a physical nature and which describe the behavior of concrete very well within a particular task. This paper presents an example of such constitutive relations which have the potential for implementation and application in finite element systems. Specifically, constitutive relations for modeling the plane stress state of concrete are presented and subsequently tested and evaluated in this paper. The relations are based on the incremental theory of elastic strain-hardening plasticity in which a non-associated flow rule is used. The calculation result for the case of concrete under uniaxial compression is compared with the experimental data for the purpose of the validation of the constitutive relations used.

• Land and Housing Review
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• v.15 no.1
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• pp.77-97
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• 2024

This study aims to examine the distinctions in evaluation index items between overseas and domestic ecological area-related systems, derive analytical indicators, and assess recently completed apartment complexes before and after the implementation of overall ecological area ratios. The objective is to analyse variances in the application of ecological area characteristics, categorizing them into ecological area analysis indicators and presenting their implications. The spatial scope covers completed apartment complexes in both metropolitan and non-metropolitan areas. Thirty-six completed apartment complexes were selected for analysis, and basic ecological area data were compiled. Subsequently, the data was utilized to categorize three analysis indicators-ecological area ratio, ecological area diversity, and ecological area connectivity-by metropolitan and non-metropolitan areas, as well as by type of apartment complex (sale housing versus rental housing) and size (large-scale, medium-scale, and small-scale). Results of the analysis indicate higher ecological area ratios and greater diversity in ecological area spatial types in metropolitan areas compared to non-metropolitan areas, and in pre-sale housing complexes compared to rental housing complexes. Mediumand large-scale apartment complexes exhibit higher ecological area ratios, with ecological area diversity being more pronounced. Ecological area connectivity reveals more numerous and varied connection points and types in metropolitan areas than in non-metropolitan areas. Implications of this study suggest that large-scale development should prioritize securing ecological area ratios and diversity in apartment complexes. Enhancing biodiversity necessitates establishing connections within and beyond the ecological area network of the complex. Future research should focus on linking the ecological area network within the complex.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4814-4834
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• 2018

This paper presents an architecture for wireless sensor networks (WSNs) with blind source separation (BSS) applied to retrieve the received mixing signals of the sink nodes first. The little-to-no need of prior knowledge about the source signals of the sink nodes in the BSS method is obviously advantageous for WSNs. The optimization problem of the BSS of multiple independent source signals with complex and noncircular distributions from observed sensor nodes is considered and addressed. This paper applies Castella's reference-based scheme to Novey's negentropy-based algorithms, and then proposes a novel fast fixed-point (FastICA) algorithm, defined as the reference-signal negentropy complex FastICA (RSNT-cFastICA) for complex-valued noncircular-distribution source signals. The proposed method for the sink nodes is substantially more efficient than Novey's quasi-Newton algorithm in terms of computational speed under large numbers of samples, can effectively improve the power consumption effeciency of the sink nodes, and is significantly beneficial for WSNs and wireless communication networks (WCNs). The effectiveness and performance of the proposed method are validated and compared with three related BSS algorithms through theoretical analysis and simulations.