• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.55 no.10
• /
• pp.491-497
• /
• 2006

For high efficiency and easy speed control of brushless DC (BLDC) motor, the demand of BLDC motor is increasing. Especially demand of interior permanent magnet (IPM) BLDC with high efficiency and high power in electric motion vehicle is increasing. However, IPM BLDC basically has a high cogging torque that results from the interaction of permanent magnet magnetomotive force (MMF) harmonics and air-gap permeance harmonics due to slotting. This cogging torque generates vibration and acoustic noises during the driving of motor. Thus reduction of the cogging torque has to be considered in IPM BLDC motor design by analytical methods. This paper proposes the cogging torque reduction method for IPM BLDC motor. For reduction of cogging torque of IPM BLDC motor, this paper describes new technique of the flux barriers design. The proposed method uses sinusoidal form of flux density to reduce the cogging torque. To make the sinusoidal air-gap flux density, flux barriers are applied in the rotor and flux barriers that installed in the rotor produce the sinusoidal form of flux density. Changing the number of flux barrier, the cogging torque is analyzed by finite element method. Also characteristics of designed model by the proposed method are analyzed by finite element method.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.156-156
• /
• 2022

Explicitly spatially distributed and reliable data on industrial water demand is very much important for both policy makers and researchers in order to carry a region-specific analysis of water resources management. However, such type of data remains scarce particularly in underdeveloped and developing countries. Current research is limited in using different spatially available socio-economic, climate data and geographical data from different sources in accordance to predict industrial water demand at finer resolution. This study proposes a random forest regression (RFR) model to predict the industrial water demand at 0.50× 0.50 spatial resolution by combining various features extracted from multiple data sources. The dataset used here include National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL), Global Power Plant database, AQUASTAT country-wise industrial water use data, Elevation data, Gross Domestic Product (GDP), Road density, Crop land, Population, Precipitation, Temperature, and Aridity. Compared with traditional regression algorithms, RF shows the advantages of high prediction accuracy, not requiring assumptions of a prior probability distribution, and the capacity to analyses variable importance. The final RF model was fitted using the parameter settings of ntree = 300 and mtry = 2. As a result, determinate coefficients value of 0.547 is achieved. The variable importance of the independent variables e.g. night light data, elevation data, GDP and population data used in the training purpose of RF model plays the major role in predicting the industrial water demand.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.9 no.3
• /
• pp.30-40
• /
• 2010

The freeway congestion is largely generated by a mainline spillover of the exit ramp queue. So it is necessary to study for modeling of the phenomenon and applying the model. In this study, the authors evaluated applicability of the Supply-Demand model, which can express traffic flow for the freeway by applying flexibly supply and demand curves for capacity of the freeway. First the authors proposed methods processing input data required in the Supply-Demand model, such as sending & receiving functions and time-varying capacity constraints for the freeway mainline. After modeling the Supply-Demand application model, the authors applied the model to the site including congested Hongeun exit ramp in Seoul Ring-road, and improved the model by adjusting application techniques and calibrating parameters. The result of the analysis showed that the Supply-Demand model yielded a queuing pattern and queue location similar to them observed in the field data, and applicability of the Supply-Demand model was varified.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2011.02a
• /
• pp.77-77
• /
• 2011

These days, a growing demand for memory device is filled up with the flash memory and the dynamic random access memory (DRAM). Although DRAM is a reasonable solution for current demand, the universal novel memory with high density, high speed and nonvolatility, needs to be developed. Among various new memories, the magnetic random access memory (MRAM) device is considered as one of good candidate memories because of excellent features including high density, high speed, low operating power and nonvolatility. The etching of MTJ stack which is composed of magnetic materials and insulator such as MgO is one of the vital process for MRAM. Recently, MgO has attracted great interest in the MTJ stack as tunneling barrier layer for its high tunneling magnetoresistance values. For the successful realization of high density MRAM, the etching process of MgO thin films should be investigated. Until now, there were some works devoted to the investigations on etch characteristics of MgO thin films. Initially, ion milling was applied to the etching of MgO thin films. However, ion milling has many disadvantages such as sidewall redeposition and etching damage. High density plasma etching containing the magnetically enhanced reactive ion etching and high density reactive ion etching have been employed for the improvement of etching process. In this work, inductively coupled plasma reactive ion etching (ICPRIE) system was adopted for the improvement of etching process using MgO thin films and etching gas mixes of $CH_4$/Ar and $CH_4$/$O_2$/Ar have been employed. The etch rates are measured by a surface profilometer and etch profiles are observed using field emission scanning emission microscopy (FESEM). The effects of gas concentration and etch parameters such as coil rf power, dc-bias voltage to substrate, and gas pressure on etch characteristics will be systematically explored.

• Journal of Drive and Control
• /
• v.21 no.2
• /
• pp.53-64
• /
• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• Membrane and Water Treatment
• /
• v.11 no.3
• /
• pp.217-222
• /
• 2020

An electrochemical oxidation process was applied to remove cyanide (CN^-) from real plating wastewater. CN^- removal efficiencies were investigated under various operating factors: current density and electrolyte concentration. Electrolyte concentration positively affected the removal of both CN^- and Chemical Oxygen Demand (COD). As the electrolyte concentration increased from 302 to 2,077 mg Cl^-/L, removal efficiency of CN^- and COD increased from 49.07% to 98.30% and from 23.53% to 49.50%, respectively, at 10 mA/㎠. Current density affected the removal efficiency in a different way. As current density increased at a fixed electrolyte concentration, CN^- removal efficiency increased while COD removal efficiency decreased, this is probably due to lowered current efficiency caused by water electrolysis.

• Journal of the Korean housing association
• /
• v.12 no.2
• /
• pp.151-160
• /
• 2001

The purpose of this study is for investigating characteristics of changing residential density and relations between housing supply and redistribution of population in Daegu. As a result, the following conclusions could be made. First, land development and housing supply have important effect on distribution of population and changing residential density in Daegu. Second, according to massive site development to meet the housing demand in suburban area, the suburbanization has begun and build-up-area grows faster, so centre of gravity of residential density increasing rate has moved to the suburban. Third, the inner district and the district near urban centre will need remodelling or redevelopment project in the near future.

• Journal of Environmental Science International
• /
• v.22 no.8
• /
• pp.965-977
• /
• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

• Korean Journal of Materials Research
• /
• v.32 no.11
• /
• pp.474-480
• /
• 2022

Lithium-ion batteries (LIBs) are powerful energy storage devices with several advantages, including high energy density, large voltage window, high cycling stability, and eco-friendliness. However, demand for ultrafast charge/discharge performance is increasing, and many improvements are needed in the electrode which contains the carbon-based active material. Among LIB electrode components, the conductive additive plays an important role, connecting the active materials and enhancing charge transfer within the electrode. This impacts electrical and ionic conductivity, electrical resistance, and the density of the electrode. Therefore, to increase ultrafast cycling performance by enhancing the electrical conductivity and density of the electrode, we complexed Ketjen black and graphene and applied conductive agents. This electrode, with the composite conductive additives, exhibited high electrical conductivity (12.11 S/cm), excellent high-rate performance (28.6 mAh/g at current density of 3,000 mA/g), and great long-term cycling stability at high current density (88.7 % after 500 cycles at current density of 3,000 mA/g). This excellent high-rate performance with cycling stability is attributed to the increased electrical conductivity, due to the increased amount of graphene, which has high intrinsic electrical conductivity, and the high density of the electrode.

• Proceeding of EDISON Challenge
• /
• 2017.03a
• /
• pp.128-131
• /
• 2017

We explore the inverse electron demand Diels-Alder reactions of tetrazines with various functional groups employing quantum calculations. In general, the rate of inverse electron demand Diels-Alder reaction depends on molecular orbital levels of electron donor and electron acceptor. Likewise, ${\pi}$ orbital of the dienophile and ${\pi}^*$ orbital of the diene is a key factor. In this work, we discuss the case where the energy of diene's ${\pi}^*$ molecular orbital is not the sole governing factor to determine the reaction rate, rather the rate shows strong correlation with the charge density of dienes.