• Journal of Biosystems Engineering
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• v.39 no.4
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• pp.377-388
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• 2014

Purpose: Determining the spatial structure of data is important in understanding within-field variability for site-specific crop management. An understanding of the spatial structures present in the data may help illuminate interrelationships that are important in subsequent explanatory analyses, especially when site variables are correlated or are a combined response to multiple causative factors. Methods: In this study, correlation, principal component analysis, and single and nested variogram models were applied to soil electrical conductivity and chemical property data of two fields in central Missouri, USA. Results: Some variables that were highly correlated, or were strongly expressed in the same principal component, exhibited similar spatial ranges when fitted with a single variogram model. However, single variogram results were dependent on the active lag distance used, with short distances (30 m) required to fit short-range variability. Longer active lag distances only revealed long-range spatial components. Nested models generally yielded a better fit than single models for sensor-based conductivity data, where multiple scales of spatial structure were apparent. Gaussian-spherical nested models fit well to the data at both short (30 m) and long (300 m) active lag distances, generally capturing both short-range and long-range spatial components. As soil conductivity relates strongly to profile texture, we hypothesize that the short-range components may relate to the scale of erosion processes, while the long-range components are indicative of the scale of landscape morphology. Conclusion: In this study, we investigated the effect of changing active lag distance on the calculation of the range parameter. Future work investigating scale effects on other variogram parameters, including nugget and sill variances, may lead to better model selection and interpretation. Once this is achieved, separation of nested spatial components by factorial kriging may help to better define the correlations existing between spatial datasets.

• Journal of Korean Medicine for Obesity Research
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• v.8 no.1
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• pp.63-70
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• 2008

Objectives Obesity is the major risk factor of the heart disease and the metabolic disease. The autonomic nervous system is a key contributor in the regulation of energy balance, so the blunted activity may contribute to the maintenance of the obese state. So we evaluated the function of the autonomic nervous system in young overweight and obese women with heart rate variability. Methods The subjects were 26 overweight and obese young women(BMI〉$23kg/m^2$) and 25 lean women who visited Kangnam Kyunghee hospital for obesity management from March 2006 to April 2008. Bioimpedance Analysis (BIA), short-term spectral analysis of heart rate variability (HRV) were performed. Results HRV of obese young group was lower than the lean young group, but there is no statistical significance. BMI had significantly a negative correlation with Low Frequency(LF) of HRV. LF power is mediated by sympathetic nervous system activity. These results indicate a decrease of sympathetic modulation in overweight and obese young women. Conclusions Overweight and obese young women have decreased sympathetic nervous system activity. In clinical practice, an assessement of HRV would be noninvasive and sensitive methods for sympathetic nervous system of young overweight and obese women.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.2
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• pp.146-151
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• 2005

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application. We here assume that customer order sizes are described by the Poisson distribution with the random parameter following a gamma distribution. This implies in turn that the negative binomial distribution is obtained by mixing the mean of the Poisson distribution with a gamma distribution. The purpose of this paper is to give an interpretation of the negative binomial demand process by considering the sources of variability in the unknown Poisson parameter. Such variability comes from the unknown demand rate and the unknown lead time interval.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.1
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• pp.1-11
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• 2017

Stochastic resource-constrained project scheduling problem is an extension of resource-constrained project scheduling problem such that activity duration has stochastic nature. In real situation where activity duration is not known until the activity is finished, open-loop based static policies such as activity-based policy and priority-based policy will not well cope with duration variability. Then, a dynamic policy based on closed-loop decision making will be regarded as an alternative toward achievement of minimal makespan. In this study, a dynamic policy designed to select activities to start at each decision time point is illustrated. The performance of static and dynamic policies based on variable neighborhood search is evaluated under the discrete-event simulation environment. Experiments with J120 sets in PSPLIB and several probability distributions of activity duration show that the dynamic policy is superior to static policies. Even when the variability is high, the dynamic policy provides stable and good solutions.

• Journal of Communications and Networks
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• v.14 no.6
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• pp.672-681
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• 2012

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.4
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• pp.79-84
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• 2005

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application. We here assume that customer order sizes are described by the Poisson distribution with the random parameter following a gamma distribution. This implies in turn that the negative binomial distribution is obtained by mixing the mean of the Poisson distribution with a gamma distribution. The purpose of this paper is to give an interpretation of the negative binomial demand process by considering the sources of variability in the unknown Poisson parameter. Such variability comes from the unknown demand rate and the unknown lead time interval.

• Journal of the Korea Society of Computer and Information
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• v.22 no.6
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• pp.87-93
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• 2017

In this paper, we design a small low power single channel ECG(Electrocardiogram) system of Chest Belt type with fiber-type electrodes to measure emotional state change of game addict. HRV(Heart Rate Variability) is analyzed through heart rate signal measurement and the psychological stress state is judged by using it. And it verifies its effectiveness through prototype. First, we design HR measurement module through low power MCU(Micro Controller Unit) and implement prototype level measurement system. The results showed that the difference between the addiction group and the general group was confirmed and that the system was effective. The result of this study can be used for health management such as reduction of stress of the user through music and breathing that lowers the stress by detecting the stress state of the general person or the chronic ill person.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.73-90
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• 2018

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1049-1051
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• 2003

This study investigated the use of airborne image data to provide estimates of within -field variability in soil properties and crop growth as an alternative to extensive field data collection. Hyperspectral and multispectral images were acquired in 2000, 2001, and 2002 for central Missouri experimental fields. Data were converted to reflectance using chemically-treated reference tarps with known reflectance levels. Geometric distortion of the hyperspectral pushbroom sensor images was corrected with a rubber sheeting transformation. Statistical analyses were used to relate image data to field-measured soil properties and crop characteristics. Results showed that this approach has potential; however, it is important to address a number of implementation issues to insure quality data and accurate interpretations.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.221-237
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• 2005

For developing the site-specific fertilizer management strategies of crop, it is essential to know the spatial variability of soil factors and to assess their influence on the variability of crop growth and yield. In 2002 and 2003 cropping seasons within-field spatial variability of rice growth and yield was examined in relation to spatial variation of soil properties in the· two paddy fields having each area of ca. $6,600m^2$ in Suwon, Korea. The fields were managed without fertilizer or with uniform application of N, P, and K fertilizer under direct-seeded and transplanted rice. Stable soil properties such as content of clay (Clay), total nitrogen (TN), organic mater (OM), silica (Si), cation exchange capacity (CEC), and rice growth and yield were measured in each grid of $10\times10m$. The two fields showed quite similar spatial variation in soil properties, showing the smallest coefficient of variation (CV) in Clay $(7.6\%)$ and the largest in Si $(21.4\%)$. The CV of plant growth parameters measured at panicle initiation (PIS) and heading stage (HD) ranged from 6 to $38\%$, and that of rice yield ranged from 11 to $21\%$. CEC, OM, TN, and available Si showed significant correlations with rice growth and yield. Multiple linear regression model with stepwise procedure selected independent variables of N fertilizer level, climate condition and soil properties, explaining as much as $76\%$ of yield variability, of which $21.6\%$ is ascribed to soil properties. Among the soil properties, the most important soil factors causing yield spatial variability was OM, followed by Si, TN, and CEC. Boundary line response of rice yield to soil properties was represented well by Mitcherich equation (negative exponential equation) that was used to quantify the influence of soil properties on rice yield, and then the Law of the Minimum was used to identify the soil limiting factor for each grid. This boundary line approach using five stable soil properties as limiting factor explained an average of about $50\%$ of the spatial yield variability. Although the determination coefficient was not very high, an advantage of the method was that it identified clearly which soil parameter was yield limiting factor and where it was distributed in the field.