• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.189-204
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• 2021

In a regression analysis, a single best model is usually selected among several candidate models. However, it is often useful to combine several candidate models to achieve better performance, especially, in the prediction viewpoint. Model combining methods such as stacking and Bayesian model averaging (BMA) have been suggested from the perspective of averaging candidate models. When the candidate models include a true model, it is expected that BMA generally gives better performance than stacking. On the other hand, when candidate models do not include the true model, it is known that stacking outperforms BMA. Since stacking and BMA approaches have different properties, it is difficult to determine which method is more appropriate under other situations. In particular, it is not easy to find research papers that compare stacking and BMA when regression model assumptions are violated. Therefore, in the paper, we compare the performance among model averaging methods as well as a single best model in the linear regression analysis when standard linear regression assumptions are violated. Simulations were conducted to compare model averaging methods with the linear regression when data include outliers and data do not include them. We also compared them when data include errors from a non-normal distribution. The model averaging methods were applied to the water pollution data, which have a strong multicollinearity among variables. Simulation studies showed that the stacking method tends to give better performance than BMA or standard linear regression analysis (including the stepwise selection method) in the sense of risks (see (3.1)) or prediction error (see (3.2)) when typical linear regression assumptions are violated.

• Journal of the Korean Society of Safety
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• v.24 no.2
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• pp.76-82
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• 2009

The NIOSH lifting equation has been used as a dominant tool in evaluating the hazard levels of lifting tasks. Although it provides two different ways for each simple and complex lifting task, the NIOSH simple lifting equation is almost used for not only simple tasks but also complex tasks. However, most of lifting tasks in industries are in the form of complex lifting. Therefore some errors occur inevitably in the evaluation of complex lifting tasks. Among complex lifting tasks, a multi-stacking task is the most popular in lifting tasks. To compensate the error in the evaluation of multi-stacking tasks by using the NIOSH simple lifting equation, a set of calculations for finding LIs(Lifting Indices) was performed for the systematically varying multi-stacking tasks. Then a regression model which finds the equivalent height in simple lifting task for multi-stacking task was established. By using this model, multi-stacking tasks can be evaluated with less error. To validate this model, some real multi-stacking tasks were evaluated as examples.

• The Journal of Society for e-Business Studies
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• v.25 no.3
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• pp.1-13
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• 2020

Machine learning refers to a model generation technique that can solve specific problems from the generalization process for given data. In order to generate a high performance model, high quality training data and learning algorithms for generalization process should be prepared. As one way of improving the performance of model to be learned, the Ensemble technique generates multiple models rather than a single model, which includes bagging, boosting, and stacking learning techniques. This paper proposes a new Ensemble technique with multiple stacking that outperforms the conventional stacking technique. The learning structure of multiple stacking ensemble technique is similar to the structure of deep learning, in which each layer is composed of a combination of stacking models, and the number of layers get increased so as to minimize the misclassification rate of each layer. Through experiments using four types of datasets, we have showed that the proposed method outperforms the exiting ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1200-1215
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• 2023

With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points.

• Journal of Biosystems Engineering
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• v.21 no.2
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• pp.191-197
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• 1996

Allowable stacking duration of the corrugated fiberboard boxes being widely used for packaging fruits and vegetables was analyzed by the creep behavior and the cumulative load correction factor for the boxes. The stacking boxes were assumed to be stored at a nearly constant temperature and relative humidity condition. When the stacking duration was short period, the stacking height determined by two methods showed a little difference between them, but almost no difference was shown as the stacking duration was longer. Allowable stacking duration was rapidly decreased with the increase of static load applied on the stacking boxes, and allowable stacking duration of Box A was estimated the longer than that of Box B. A model of allowable stacking duration for the corrugated fiberboard box was developed as a function of the stacking load and the ambient relative humidity.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1386-1394
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• 2022

As consumption of high-quality fruits increases and sales and packaging units become smaller, the demand for automatic fruit grading systems is increasing. Compared to other crops, the quality of fruit is determined by visual characteristics such as shape, color, and scratches, rather than just physical size and weight. Accordingly, this study presents a CNN model that can effectively extract and classify the visual features of fruits and a perceptron that classifies fruits using physical features, and proposes a stacking ensemble model that can effectively combine the classification results of these two neural networks. The experiments with AI Hub public data show that the stacking ensemble model is effective for grading fruits. However, the ensemble model does not always improve the performance of classifying all the fruit grading. So, it is necessary to adapt the model according to the kind of fruit.

• Journal of the Korean Chemical Society
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• v.30 no.3
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• pp.289-295
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• 1986

Spectroscopic studies have been carried out on the metachromatic behavior of methylene blue(MB) and acridine orange(AO) in the presence of polyvinylsulfate(PVS) and polystyrenesulfonate(PSS) The characteristic changes of meta-band with the change of P/D value are discussed in terms of stacking theory. It has been found that the stacking effect in the PVS-dye system is stronger than that in the PSS-dye system and that MB shows stronger stacking effect than AO. A stacking model and dimension of bound dyes on the surface of polymer chain is proposed on the basis of the previously suggested model of dimer found in the aqueous solution of planar aromatic dyes. The proposed model is found to be reasonable in accordance with the experimental results obtained by various workers.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.103.3-103
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• 2002

1. Introduction 2. Dynamic Model of Stacking Crane 3. The Path Plan of Stacking Crane 4. Design of Neural Network Predictive PID Controller 5. Result of Computer Simulation and Investigation 5. Conclusion

• Journal of the Korean Chemical Society
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• v.31 no.4
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• pp.295-300
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• 1987

Spectroscopic studies have been carried out on the metachromatic behavior of methylene blue(MB) and acridine orange(AO) in the presence of chondroitin sulfate A(CSA) and chondroitin sulfate C(CSC). The characteristic changes of the meta-band with the changes of P/D value are discussed in terms of the stacking theory. Quantitative studies on the stacking effect are made to calculate the number of bound molecules of dye per unit molecule of the polyanion. The result shows that MB has stronger stacking effect than AO. A stacking model and the dimension of the bound dyes on the surface of the polyanion are proposed, on the basis of the dimer model of planar aromatic dyes and the most stable conformation of the CSA chain. The model is found to be reasonable in accordance with the experimental results.

• Journal of Biosystems Engineering
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• v.20 no.4
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• pp.333-342
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• 1995

The resistance to air flow through fruits and vegetables in bulk was an important consideration in the design of the pressure cooling system. The amount of resistance to air flow through produce in bulk normally depended upon air flow rate, stacking depth, porosity, stacking patterns and shape and site of product. But, there was not enough information relating the effects of those factors on air flow resistance. The objectives of this study were to investigate the effect of stacking depth, stacking patterns, porosity and airflow rate on airflow resistance and to develop a statistical model to predict static pressure drop across the produce bed as a function of air flow rate, stacking depth, bed porosity, and product size. Mandarins and tomatoes were used in the experiment. The airflow rate were in the range of 0.1~1.0 ㎥/s.$m^2$, the porosity were in the range of 0.25~0.45, the depth were in the range of 0.3~0.9m and the equivalent diameters were 5.3cm and 6.3cm for mandarins, and 6.5cm and 8.5cm for tomatoes. Three methods of stacking arrangement were used i.e. cubic, square staggered, and staggered stacking arrangement. The results were summarized as follows. 1. The pressure drops across produce bed increased in proportion to stacking depth and superficial air velocity and decreased in proportion to porosity. 2. The increasing rates of pressure drop according to stacking patterns with the increase of superficial air velocity were different one another. The staggered stacking arrangement produced the highest increasing rate and the cubic stacking arrangement produced the lowest increasing rate. But it could be assumed that the stacking patterns had not influenced greatly on pressure drops if it was of equal porosity. 3. The statistical models to predict the pressure drop across produce bed as a function of superficial air velocity, stacking depth, porosity, and product diameter were developed from these experiments.