• Journal of Korean Institute of Industrial Engineers
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• v.38 no.1
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• pp.25-30
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• 2012

Desirability approaches have been proposed to find an optimum of multiple response problem. The existing desirability approaches use either of mean or min of individual desirability in aggregation of multiple responses. However, in order to find an optimum having high mean and low dispersion among individual desirability, the dispersion needs to be simultaneously considered with its mean. This study proposes bias and variance (BV) method which aggregates bias (ideal target-mean) and variance of individual desirability in multiple response optimization. The proposed BV method was applied to an example to evaluate its usefulness by comparing with existing methods. Evaluation results showed that the solution of BV method was a fairly good compared with DS (Derringer and Suich, 1980) and KL (Kim and Lin, 2000) methods. The BV method can be utilized to multiple response surface problems when decision makers want to find an optimum having high mean and low variance among responses.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.91-100
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• 2009

"How does advertising affect sales?" is the fundamental issue of modern advertising research. There is an interesting issue for estimating carryover effects of advertising on sales, and the aggregated data biases exist in the duration of advertising effect. This research suggests an extended model of Koyck Model which is employed for micro-data (Koyck 1954) to estimate aggregated advertising data, and empirically shows the aggregated data bias. Our developed model with the aggregated level of actual advertising data is more appropriate than the basic Koyck model for micro-data. The result figures out that it is important to consider the disaggregated data level in the analysis of dynamic effects of adverting such as carryover effects.

• ETRI Journal
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• v.42 no.5
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• pp.773-780
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• 2020

A 40 nm complementary metal oxide semiconductor carrier-aggregated drive amplifier with high linearity is presented for sub-GHz Internet of Things applications. The proposed drive amplifier consists of two high linear amplifiers, which are composed of five differential cascode cells. Carrier aggregation can be achieved by switching on both the driver amplifiers simultaneously and combining the two independent signals in the current mode. The common gate bias of the cascode cells is selected to maximize the output 1 dB compression point (P1dB) to support high-linear wideband applications, and is used for the local supply voltage of digital circuitry for gain control. The proposed circuit achieved an output P1dB of 10.7 dBm with over 22.8 dBm of output 3rd-order intercept point up to 0.9 GHz and demonstrated a 55 dBc adjacent channel leakage ratio (ACLR) for the 802.11af with -5 dBm channel power. To the best of our knowledge, this is the first demonstration of the wideband carrier-aggregated drive amplifier that achieves the highest ACLR performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.999-1005
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• 2020

While search portals' 'Portal News' account for the largest portion of aggregated news outlet, its neutrality as an outlet is questionable. This is because news aggregation may lead to prejudiced information consumption by recommending biased news articles. In this paper we introduce a new method of measuring political bias of news articles by using deep learning. It can provide its readers with insights on critical thinking. For this method, we build the dataset for deep learning by analyzing articles' bias from keywords, sourced from the National Assembly proceedings, and assigning bias to said keywords. Based on these data, news article bias is calculated by applying deep learning with a combination of Convolution Neural Network and Recurrent Neural Network. Using this method, 95.6% of sentences are correctly distinguished as either conservative or progressive-biased; on the entire article, the accuracy is 46.0%. This enables analyzing any articles' bias between conservative and progressive unlike previous methods that were limited on article subjects.

• Journal of Periodontal and Implant Science
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• v.45 no.1
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• pp.2-7
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• 2015

A fundamental problem in analyzing complex multilevel-structured periodontal data is the violation of independency among the observations, which is an assumption in traditional statistical models (e.g., analysis of variance and ordinary least squares regression). In many cases, aggregation (i.e., mean or sum scores) has been employed to overcome this problem. However, the aggregation approach still exhibits certain limitations, such as a loss of power and detailed information, no cross-level relationship analysis, and the potential for creating an ecological fallacy. In order to handle multilevel-structured data appropriately, mixed effects models have been introduced and employed in dental research using periodontal data. The use of mixed effects models might account for the potential bias due to the violation of the independency assumption as well as provide accurate estimates.

• Journal of Korean Society of Transportation
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• v.18 no.3
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• pp.55-76
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• 2000

We propose a general solution methodology for identifying the optimal aggregation interval sizes as a function of the traffic dynamics and frequency of observations for four cases : i) link travel time estimation, ii) corridor/route travel time estimation, iii) link travel time forecasting. and iv) corridor/route travel time forecasting. We first develop statistical models which define Mean Square Error (MSE) for four different cases and interpret the models from a traffic flow perspective. The emphasis is on i) the tradeoff between the Precision and bias, 2) the difference between estimation and forecasting, and 3) the implication of the correlation between links on the corridor/route travel time estimation and forecasting, We then demonstrate the Proposed models to the real-world travel time data from Houston, Texas which were collected as Part of the Automatic Vehicle Identification (AVI) system of the Houston Transtar system. The best aggregation interval sizes for the link travel time estimation and forecasting were different and the function of the traffic dynamics. For the best aggregation interval sizes for the corridor/route travel time estimation and forecasting, the covariance between links had an important effect.

• Journal of Chest Surgery
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• v.49 no.3
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• pp.145-150
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• 2016

Background: Extracorporeal circulation (ECC) can induce alterations in blood viscoelasticity and cause red blood cell (RBC) aggregation. In this study, the authors evaluated the effects of pump flow pulsatility on blood viscoelasticity and RBC aggregation. Methods: Mongrel dogs were randomly assigned to two groups: a nonpulsatile pump group (n=6) or a pulsatile pump group (n=6). After ECC was started at a pump flow rate of 80 mL/kg/min, cardiac fibrillation was induced. Blood sampling was performed before and at 1, 2, and 3 hours after ECC commencement. To eliminate bias induced by hematocrit and plasma, all blood samples were adjusted to a hematocrit of 45% using baseline plasma. Blood viscoelasticity, plasma viscosity, hematocrit, arterial blood gas analysis, central venous $O_2$ saturation, and lactate were measured. Results: The blood viscosity and aggregation index decreased abruptly 1 hour after ECC and then remained low during ECC in both groups, but blood elasticity did not change during ECC. Blood viscosity, blood elasticity, plasma viscosity, and the aggregation index were not significantly different in the groups at any time. Hematocrit decreased abruptly 1 hour after ECC in both groups due to dilution by the priming solution used. Conclusion: After ECC, blood viscoelasticity and RBC aggregation were not different in the pulsatile and nonpulsatile groups in the adult dog model. Furthermore, pulsatile flow did not have a more harmful effect on blood viscoelasticity or RBC aggregation than nonpulsatile flow.

• Journal of Environmental Health Sciences
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• v.42 no.5
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• pp.302-313
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• 2016

Objectives: This study was conducted to assess environmental health status on a local scale using environmental health-related indicators. It demonstrated the possibility of using a structural equation model, a methodological approach to provide synthesized information. Methods: Eighteen indicators were selected from official statistical data published by local governments. Each environmental health-related indicator was classified according to the PSR (pressure-state-response) model. Aggregation methods were performed using principal component analysis and fuzzy sets. Results: The five principal components were classified through principal component analysis (PCA) and obtained eigenvalues >1.0 from the initial 18 indicators. The aggregated index was obtained by condensing the original information into two broad and simple categories through fuzzy sets. Conclusion: This could be useful in that the aggregation procedure may provide a basis for establishing environmental health policies and a decision-making process. However, the availability and quality of indicators, assessment of aggregation method bias, choice of weighted scores for indicators, and other factors should be examined in future studies.

• Journal of Ecology and Environment
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• v.35 no.4
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• pp.359-365
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• 2012

We investigated leaf disease intensity of Kalopanax septemlobus (prickly castor oil tree) caused by the parasitic fungus Mycosphaerella acanthopanacis, in thirty natural host populations in the Japanese Archipelago. The disease intensity observed for individual trees were analyzed using a generalized additive model as a function of tree size, tree density, climatic terms and spatial trend surface. Individual tree size and conspecific tree density were shown to have significant negative and positive effects on disease intensity, respectively. The findings suggest that the probability of disease infection is partly determined by dispersal of infection agents (ascospores) from the fallen leaves on the ground, which can be enhanced by aggregation of host trees in a forest stand. Regional-scale spatial bias was also present in disease intensity; the populations in northern Japan and southern Kyushu were more severely infected by the fungus than those in southwestern Honshu and Shikoku. Regional variation of disease intensity was explained by both climatic factors and a trend surface term, with a latitudinal cline detected, which increases towards the north. Further research should be conducted in order to understand all of the factors generating the latitudinal cline detected in this study.