• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.859-864
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• 2015

This paper analyzes the impact of Plug-in Electric vehicles(PEVs) on power demand and voltage change when PEVs are connected to the domestic distribution system. Specifically, it assesses PEVs charging load by charging method in accordance with PEVs penetration scenarios, its percentage of total load, and voltage range under load conditions. Concretely, we develop EMTDC modelling to perform a voltage distribution analysis when the PEVs charging system by their charging scenario was connected to the distribution system under the load condition. Furthermore we present evaluation algorithm to determine whether it is possible to adjust it such that it is in the allowed range by applying ULTC when the voltage change rate by PEVs charging scenario exceed its allowed range. Also, detailed analysis of the impact of PEVs on power distribution system was carried out by calculating existing electric power load and additional PEVs charge load by each scenario on new-town in Korea to estimate total load increases, and also by interpreting the subsequent voltage range for system circuits and demonstrating conditions for countermeasures. It was concluded that total loads including PEVs charging load on new-town distribution system in Korea by PEVs penetration scenario increase significantly, and the voltage range when considering ULTC, is allowable in terms of voltage tolerance range up to a PEVs penetration of 20% by scenario. Finally, we propose the charging capacity of PEVs that can delay the reinforcement of power distribution system while satisfying the permitted voltage change rate conditions when PEVs charging load is connected to the power distribution system by their charging penetration scenario.

• Journal of Environmental Impact Assessment
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• v.9 no.3
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• pp.185-202
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• 2000

Uncertainty and variability in Life Cycle Assessment(LCA) have been significant key issues in LCA methodology with techniques in other research area such as social and political science. Variability is understood as stemming from inherent variations in the real world, while uncertainty comes from inaccurate measurements, lack of data, model assumptions, etc. Related articles in this issues were reviewed for classification, distinguish and elaboration of probabilistic/stochastic health risk analysis application in LCA. Concept of focal zone, streamlining technique, scenario modelling and Monte Carlo/Latin Hypercube risk analysis were applied to the uncertainty/variability analysis of health risk in LCA. These results show that this general framework of multi-disciplinary methodology between probabilistic health risk assessment and LCA was of benefit to decision making process by suppling information about input/output data sensitivity, health effect priority and health risk distribution. There should be further research needs for case study using this methodology.

• Journal of Energy Engineering
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• v.21 no.1
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• pp.33-42
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• 2012

In this study, long-term greenhouse gas reductions expected passenger sector was used for the MESSAGE. Green Car road map proposed BAU scenario, Enhanced diffusion green car scenario, and price 1, 2 scenarios was configured with four scenarios. Enhanced diffusion green car in the scenario, in 2050 compared to BAU scenario 13% of the emissions will decrease. Price 1 and Price 2 scenario is emissions reduction of 14% compared to BAU. This study consists of six chapters. Introduction of MESSAGE, creation and RES in the year and the target year set a different base line and the passenger building materials sector activities, steps for passenger sector scenario and Based on the results of running the emissions reductions were to describe.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.3
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• pp.367-375
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• 2018

The present paper considers the contact between energy-saving device of ice-class vessel and ice block. The main objective of this study is to clarify the tendency of the ice impact force and the structural response as well as interaction effects of them. The contact analysis is performed by using LS-DYNA finite element code. The main collision scenario is based on Finnish-Swedish ice class rules and a stern duct model is used as an energy-saving device. For the contact force, two modelling approaches are adopted. One is dynamic indentation model of ice block based on the pressure-area curve. The other is numerical material modelling by LS-DYNA. The authors investigated the sensitivity of the structural response against the ice contact pressure, the interaction effect between structure and ice block, and the influence of eccentric collision. The results of these simulations are presented and discussed with respect to structural safety.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4759-4780
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• 2017

Full Dimension multiple input multiple output (FD-MIMO) architecture employs a planar array design at the Base Station (BS) to provide high order multi-user MIMO (MU-MIMO) via simultaneous data transmission to large number of users. With FD-MIMO, the BS can also adjust the beam direction in both elevation and azimuth direction to concentrate the energy on the user of interests while minimizing the interference leakage to co-scheduled users in the same cell or users in the neighboring cells. In a typical highly populated macrocell environment, modelling the elevation angular characteristics of three-dimensional (3D) channel is critical to understanding the performance limits of the FD-MIMO system. In this paper, we study the throughput performance of FD-MIMO system with varying elevation angular spread and inter-element spacing using a 3D spatial channel model. Our results show that for a typical urban scenario, horizontal beamforming with correlated antenna spacing achieves optimal performance but by restricting the spread of elevation angles of departure, elevation beamforming achieves high array gain with wide inter-element spacing. We also realize significant gains due to spatial array processing via modelling the elevation domain and varying the inter-element spacing for both the transmitter and receiver.

• The KSFM Journal of Fluid Machinery
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• v.19 no.4
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• pp.54-62
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• 2016

This study presents an approach of tidal farming optimization using a numerical modelling method to simulate tidal energy extraction for 1MW scale tidal stream devices around Jangjuk-sudo, South Korea. The utility of the approach in this research is demonstrated by optimizing the tidal farm in an idealized scenario and a more realistic case with three scenarios of 28-turbine centered tidal array (named A, B and C layouts) inside the Jangjuk-sudo. In addition, the numerical method also provides a pre-processing calculation helps the researchers to quickly determine where the best resource site is located when considering the position of the tidal stream turbine farm. From the simulation results, it is clearly seen that the net energy (or wake energy yield which includes the impacts of wake effects on power generation) extracted from the layout A is virtually equal to the estimates of speed-up energy yield (or the gross energy which is the sum of energy yield of each turbine without wake effects), up to 30.3 GWh/year.

• Journal of Platform Technology
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• v.9 no.1
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• pp.3-14
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• 2021

Cognitive Radio network (CR) is a promising paradigm that helps the unlicensed user (Secondary User) to analyse the spectrum and coordinate the spectrum access to support the creation of common control channel (CCC). The cooperation of secondary users and broadcasting between them is done through transmitting messages in CCC. In case, if the control channels may get jammed and it may directly degrade the network's performance and under such scenario jammers will devastate the control channels. Hopping sequences may be one of the predominant approaches and it may be used to fight against this problem to confront jammer. The jamming attack can be alleviated using one of the game modelling approach and in this proposed scheme stochastic games has been analysed with more single users to provide the flexible control channels against intrusive attacks by mentioning the states of each player, strategies ,actions and players reward. The proposed work uses a modern player action and better strategic view on game theoretic modelling is stochastic game theory has been taken in to consideration and applied to prevent the jamming attack in CR network. The selection of decision is based on Q learning approach to mitigate the jamming nodes using the optimal MDP decision process

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.275-276
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• 2006

An intelligent arm-wrestling system is recently developed in our laboratory that is comprised of an arm-force generation mechanism and a control system that detects the maximum arm-force of a user in the early stage of the match, generates a different game scenario each time, and executes force feedback control to implement the scenario. This paper presents the mathematical model of the force control system of the intelligent arm-wrestling system, and some improvements of it via experimental frequency responses using a control signal analyzer.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.214-216
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• 1998

Parameterizing a model is one of the most efficient ways of conducting "virtual prototying" i.e. exploring the "What if?" scenario. But it is very difficult to construct parameterized models in commercial based FEM programs, because they usually adopt the mouse inputs in their GUI, which cannot be parameterized. We consolidated a parametric modelling technique in OPERA-3d preprocessor, which is one of world leading electromagnetic analysis programs, by combining the mouse inputs in GUI with it's FORTRAN-based self script command language.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.