• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.127-132
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• 2017

In this paper, we propose a controller gain based on model based design and implement the two-wheel inverted pendulum type robot using NXT Lego and RobotC language. Two-wheel inverted pendulum robot consists of NXT mindstorm, servo DC motor with encoder, gyro sensor, and accelerometer sensor. We measurement wheel angle using bulit-in encoder and calculate wheel angle speed using moving average method. Gyro measures body angular velocity and accelerometer measures body pitch angle. We calculate body angle with complementary filter using gyro and accelerometer sensor. The control gain is a weighted value for wheel angle, wheel angular velocity, body pitch angle, and body pich angular velocity, respectively. We experiment and observe the effect of two-wheel inverted pendulum with respect to change of control gains.

• Journal of environmental and Sanitary engineering
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• v.16 no.1
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• pp.1-8
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• 2001

Since most people spend over 80% of their time indoor, indoor air quality tends to be the dominant contributor to personal exposure. In this study, indoor and outdoor $NO_2$concentrations were measured and compared with simultaneously personal exposures of 27 house-wives and female workers of kindergarten. Time activity pattern and house characteristics were used to determine the effects of these factors on personal exposure. Since house-wives student spent most their times in indoor with mean of 89.8%, their $NO_2$ exposure was associated with indoor $NO_2$ level(r= 0.92) rather than outdoor $NO_2$ level(r= 0.87). female workers were also associated with indoor $NO_2$ level(r= 0.70) though sample number were small. Using time-weighted average model, $NO_2$ exposures of house-wives were estimated by $NO_2$ measurements in indoor home and outdoor home levels. Estimated $NO_2$ personal exposures were significantly correlated with measured $NO_2$ personal exposures (r= 0.90). These results might mean that air pollutants exposure of old and feeble persons, and infants could be estimated by measuring concentrations of indoor home.

• Journal of Environmental Science International
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• v.21 no.10
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• pp.1171-1179
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• 2012

Headwaters initiate material export to downstream environments. A nested headwater study examined the flux of dissolved constituents and water from a perennial stream and four ephemeral/intermittent streams in the Upper Gulf Coastal Plain of Mississippi. Water was collected during storm and baseflow conditions. Multiple linear regression was used to model constituent concentration and calculate flux. Event was the major source of water discharged from the ephemeral and intermittent streams however, baseflow was the major source for water discharged by the perennial stream during events. The perennial stream had an area weighted average yields of 10.1, 0.01, 1.03, 0.65 kg/ha/yr of DON (dissolved organic nitrogen), $NO_3^-$-N, $NH_4^+$-N and $PO_4^{-3}$, respectively while large variabilities existed between the ephemeral and intermittent streams. These findings highlight the importance of headwaters in protecting the low order drainage basins as a key to water quality within perennial streams.

• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.1
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• pp.11-24
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• 2017

The present work focuses multi-objective optimization of blade sweep for a Wells turbine. The blade-sweep parameters at the mid and the tip sections are selected as design variables. The peak-torque coefficient and the corresponding efficiency are the objective functions, which are maximized. The numerical analysis has been carried out by solving 3D RANS equations based on k-w SST turbulence model. Nine design points are selected within a design space and the simulations are run. Based on the computational results, surrogate-based weighted average models are constructed and the population based multi-objective evolutionary algorithm gave Pareto optimal solutions. The peak-torque coefficient and the corresponding efficiency are enhanced, and the results are analysed using CFD simulations. Two extreme designs in the Pareto solutions show that the peak-torque-coefficient is increased by 28.28% and the corresponding efficiency is decreased by 13.5%. A detailed flow analysis shows the separation phenomena change the turbine performance.

• Journal of Korea Water Resources Association
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• v.36 no.4
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• pp.597-608
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• 2003

A transcritical flow occurs when the width and slope of a channel are varying abruptly. In this study, the transcritical flow in a two-dimensional open channel is analyzed by using the shallow-water equations. A weighted average flux scheme that has flux limiter with a total variation diminishing condition is introduced for a second-order accuracy in time and space, and non- spurious oscillations at discontinuous points. A HLLC method with three wane speeds is employed to calculate the Riemann problem. To overcome difficulties resulting from variation of channel sections in a two-dimensional analysis of transcritical flow, the numerical model is developed based on a generalized grid system.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.26-30
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• 2019

This paper proposes a new voice activity detection (VAD) method which is based on SNR and non-intrusive speech intelligibility estimation. In the conventional SNR-based VAD methods, voice activity probability is obtained by estimating frame-wise SNR at each spectral component. However these methods lack performance in various noisy environments. We devise a hybrid VAD method that uses non-intrusive speech intelligibility estimation as well as SNR estimation, where the speech intelligibility score is estimated based on deep neural network. In order to train model parameters of deep neural network, we use MFCC vector and the intrusive speech intelligibility score, STOI (Short-Time Objective Intelligent Measure), as input and output, respectively. We developed speech presence measure to classify each noisy frame as voice or non-voice by calculating the weighted average of the estimated STOI value and the conventional SNR-based VAD value at each frame. Experimental results show that the proposed method has better performance than the conventional VAD method in various noisy environments, especially when the SNR is very low.

• Smart Structures and Systems
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• v.24 no.2
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• pp.233-242
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• 2019

In this paper, an inverse approach based on uniform load surface (ULS) is presented for structural damage localization and quantification. The ULS is excellent approximation for deformed configuration of a structure under distributed unit force applied on all degrees of freedom. The ULS make use of natural frequencies and mode shapes of structure and in mathematical point of view is a weighted average of mode shapes. An objective function presented to damage detection is discrepancy between the ULS of monitored structure and numerical model of structure. Solving this objective function to find minimum value yields damage's parameters detection. The teaching-learning based optimization algorithm has been employed to solve inverse problem. The efficiency of present damage detection method is demonstrated through three numerical examples. By comparison between proposed objective function and another objective function which make use of natural frequencies and mode shapes, it is revealed present objective function have faster convergence and is more sensitive to damage. The method has good robustness against measurement noise and could detect damage by using the first few mode shapes. The results indicate that the proposed method is reliable technique to damage detection in structures.

• Korean Journal of Ecology and Environment
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• v.54 no.1
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• pp.71-77
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• 2021

Using data from flies collected with pitfall traps in 365 forests on a nationwide scale in Korea, the abundance and distribution changes of two families (Phoridae and Lauxaniidae) in Korean forests were predicted at the genus level according to two climate change scenarios: RCP 4.5 and RCP 8.5. The most suitable temperature for the 17 major genera was estimated using a weighted average regression model. Stichillus and Anevrina displayed the lowest optimum temperature with 7.6℃ and 8.5℃ in annual mean temperature, respectively, whereas Chonocephalus had the highest optimum temperature with 12.1℃. Among thirty genera, seven genera (four from Phoridae and three from Lauxaniidae), which showed their abundance in a bell-type or linear pattern along the temperature gradient, were used for predicting the distribution changes according to the future climate change scenarios. All the taxa of this study are expected to decrease in abundance and distribution as a function of temperature increase. Moreover, cold-adapted taxa were found to be more affected than warm-adapted taxa.

• Asia-Pacific Journal of Business
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• v.15 no.2
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• pp.71-88
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• 2024

Purpose - This study provides an overview of existing research and practices related to market risk premiums(MRP), and empirically estimates the MRP in Korea, particularly using the related option prices. We also seek to improve the current MRP practices and explore alternative solutions. Design/methodology/approach - We present the option price-based MRP estimation method, as proposed by Martin (2017), and implement it within the context of the Korean stock market. We then juxtapose these results with those derived from other methods, and compare the characteristics with those of the United States. Findings - We found that the lower limit of the MRP in the Korean stock market shows a much lower value compared to the US. There seems to be the possibility of a market crash, exchange rate volatility, or a lack of option trading data. We investigated the predictive power of the estimated values and discovered that the weighted average of the results of various methodologies using the Principal Component Analysis (PCA) is superior to the individual method's results. Research implications or Originality - It is required to explore various methods of estimating MRP that are suitable for the Korean stock market. In order to improve the estimation methodology based on option prices, it is necessary to develop the methods using the higher-order(third order or above) moments, or consider additional risk factors such as the possibility of a crash.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2835-2841
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• 2024

Real-time analysis of metallic mineral grade and slurry concentration is significant for improving flotation efficiency and product quality. This study proposes an online detection method of ore slurry combining the Prompt Gamma Neutron Activation Analysis (PGNAA) technology and artificial neural network (ANN), which can provide mineral information rapidly and accurately. Firstly, a PGNAA analyzer based on a D-T neutron generator and a BGO detector was used to obtain a gamma-ray spectrum dataset of ore slurry samples, which was used to construct and optimize the ANN model for adaptive analysis. The evaluation metrics calculated by leave-one-out cross-validation indicated that, compared with the weighted library least squares (WLLS) approach, ANN obtained more precise and stable results, with mean absolute percentage errors of 4.66% and 2.80% for Fe grade and slurry concentration, respectively, and the highest average standard deviation of only 0.0119. Meanwhile, the analytical errors of the samples most affected by matrix effects was reduced to 0.61 times and 0.56 times of the WLLS method, respectively.