• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.5
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• pp.101-108
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• 2019

The domestic duck industry is the sixth-largest among the livestock industries. However, 34.3% of duck houses were the duck houses arbitrarily converted from plastic greenhouses. This type of duck house was difficult to properly manage internal air temperature and humidity environment. Humidity environment inside duck houses is an important factor that directly affects the productivity and disease occurrence of the duck. Although the humidity environments of litters (bedding materials) affect directly the inside environment of duck houses, there are only few studies related to humidity environment of litters. In this study, evaporation rates from litters were evaluated according to air temperature, relative humidity, water contents of litters, and wind speed. The experimental chamber was made to measure evaporation rates from litters. Temperature and humidity controlled chamber was utilized during the conduct of the laboratory experiments. Using the measured data, a multi linear regression analysis was carried out to derive the calculation formula of evaporation rates from litters. In order to improve the accuracy of the multi linear regression model, the partial vapor pressure directly related to evaporation was also considered. Variance inflation factors of air temperature, relative humidity, partial vapor pressure, water contents of litters, and wind speed were calculated to identify multicollinearity problem. The Multiple $R^2$ and adjusted-$R^2$ of regression model were calculated at 0.76 and 0.71, respectively. Therefore, the regression models were developed in this study can be used to estimate evaporation rates from the litter of duck houses.

• The Journal of Advanced Prosthodontics
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• v.11 no.5
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• pp.271-279
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• 2019

PURPOSE. The newest technologies for digital implant impression (DII) taking are developing rapidly and showing acceptable clinical results. However, scientific literature is lacking data from clinical studies about the accuracy of DII. The aim of this study was to compare digital and conventional dental implant impressions (CII) in a clinical environment. MATERIALS AND METHODS. Twenty-four fixed zirconia restorations supported by 2 implants were fabricated using conventional open-tray impression technique with splinted transfers (CII group) and scan with Trios 3 IOS (3Shape) (DII group). After multiple verification procedures, master models were scanned using laboratory scanner D800 (3Shape). 3D models from conventional and digital workflow were imported to reverse engineering software and superimposed with high resolution 3D CAD models of scan bodies. Distance between center points, angulation, rotation, vertical shift, and surface mismatch of the scan bodies were measured and compared between conventional and digital impressions. RESULTS. Statistically significant differences were found for: a) inter-implant distance, b) rotation, c) vertical shift, and d) surface mismatch differences, comparing DII and CII groups for mesial and distal implant scan bodies ($P{\leq}.05$). CONCLUSION. Recorded linear differences between digital and conventional impressions were of limited clinical significance with two implant-supported restorations.

• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.333-342
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• 1997

The objective of this research was to develop model equations for measuring rice milling ratio by using visible / HIR spectroscopy. Twelve kinds of brown rice(n = 149) were milled to obtain various milling ratio ranged from 86% to 94%. Visible/NIR spectra were collected with a spectrophotometer with sample transport module. The reflectance and transmission spectra were measured in the range of 400~2, 500nm and 600~1, 400nm, respectively, with 2 nm intervals. Multiple linear regression(MLR), Partial least square (PLS), and Artificial neural network(ANN) were used to develop models. Model developed with reflectance spectra showed better prediction results then those with transmission spectra. The MLR model with six-wavelength obtained from first derivative spectra gave to the best results for measuring the rice milling ratio(SEP = 0.535, , $r^2$ = 0.980). The PLS model(SEP = 0.604, $r^2$= 0.976) and ANN model(SEP = 0.566, $r^2$= 0.978) also can be used to determine the rice milling ratio effectively.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.259-272
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• 2022

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.

• Computers and Concrete
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• v.5 no.6
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• pp.573-597
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• 2008

Among artificial intelligence-based computational techniques, adaptive neuro-fuzzy inference systems (ANFIS) are particularly suitable for modelling complex systems with known input-output data sets. Such systems can be efficient in modelling non-linear, complex and ambiguous behaviour of cement-based materials undergoing single, dual or multiple damage factors of different forms (chemical, physical and structural). Due to the well-known complexity of sulfate attack on cement-based materials, the current work investigates the use of ANFIS to model the behaviour of a wide range of self-consolidating concrete (SCC) mixture designs under various high-concentration sodium sulfate exposure regimes including full immersion, wetting-drying, partial immersion, freezing-thawing, and cyclic cold-hot conditions with or without sustained flexural loading. Three ANFIS models have been developed to predict the expansion, reduction in elastic dynamic modulus, and starting time of failure of the tested SCC specimens under the various high-concentration sodium sulfate exposure regimes. A fuzzy inference system was also developed to predict the level of aggression of environmental conditions associated with very severe sodium sulfate attack based on temperature, relative humidity and degree of wetting-drying. The results show that predictions of the ANFIS and fuzzy inference systems were rational and accurate, with errors not exceeding 5%. Sensitivity analyses showed that the trends of results given by the models had good agreement with actual experimental results and with thermal, mineralogical and micro-analytical studies.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2410-2413
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• 2005

Finite element method (FEM) has been widely used as a useful numerical method that can analyze complex engineering problems in electro-magnetics, mechanics, and others. CEMTool, which is similar to MATLAB, is a command style design and analyzing package for scientific and technological algorithm and a matrix based computation language. In this paper, we present new 3D FEM package in CEMTool environment. In contrast to the existing CEMTool 2D FEM package and MATLAB PDE (Partial Differential Equation) Toolbox, our proposed 3D FEM package can deal with complex 3D models, not a cross-section of 3D models. In the pre-processor of 3D FEM package, a new 3D mesh generating algorithm can make information on 3D Delaunay tetrahedral mesh elements for analyses of 3D FEM problems. The solver of the 3D FEM package offers three methods for solving the linear algebraic matrix equation, i.e., Gauss-Jordan elimination solver, Band solver, and Skyline solver. The post-processor visualizes the results for 3D FEM problems such as the deformed position and the stress. Consequently, with our new 3D FEM toolbox, we can analyze more diverse engineering problems which the existing CEMTool 2D FEM package or MATLAB PDE Toolbox can not solve.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.681-692
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• 2018

This study was performed to select the significant wavelengths for predicting the chlorophyll content of grafted cucumber seedlings using hyperspectral images. The visible and near-infrared (VNIR) images and the short-wave infrared images of cucumber cotyledon samples were measured by two hyperspectral cameras. A correlation coefficient spectrum (CCS), a stepwise multiple linear regression (SMLR), and partial least squares (PLS) regression were used to determine significant wavelengths. Some wavelengths at 501, 505, 510, 543, 548, 619, 718, 723, and 727 nm were selected by CCS, SMLR, and PLS as significant wavelengths for estimating chlorophyll content. The results from the calibration models built by SMLR and PLS showed fair relationship between measured and predicted chlorophyll concentration. It was concluded that the hyperspectral imaging technique in the VNIR region is suggested effective for estimating the chlorophyll content of grafted cucumber leaves, non-destructively.

• Korean Journal of Agricultural Science
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• v.38 no.4
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• pp.761-767
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• 2011

The objective of this study was to predict total bacteria count of pork meats by using the portable electronic nose systems developed throughout two stages of the prototypes. Total bacteria counts were measured for pork meats stored at $4^{\circ}C$ for 21days and compared with the signals of the electronic nose systems. PLS(Partial least square), PCR (Principal component regression), MLR (Multiple linear regression) models were developed for the prediction of total bacteria count of pork meats. The coefficient of determination ($R_p{^2}$) and root mean square error of prediction (RMSEP) for the models were 0.789 and 0.784 log CFU/g with the 1st system for the pork loin, 0.796 and 0.597 log CFU/g with the 2nd system for the pork belly, and 0.661 and 0.576 log CFU/g with the 2nd system for the pork loin respectively. The results show that the developed electronic system has potential to predict total bacteria count of pork meats.

• Computers and Concrete
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• v.27 no.3
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• pp.185-197
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• 2021

Throughout the previous years, many efforts focused on incorporating non-biodegradable wastes as a partial replacement and sustainable alternative for natural aggregates in cement-based materials. Currently, rubberized concrete is considered one of the most important green concrete materials produced by replacing natural aggregates with rubber particles from old tires in a concrete mixture. The main benefits of this material, in addition to its importance in sustainability and waste management, comes from the ability of rubber to considerably damp vibrations, which, when used in reinforced concrete structures, can significantly enhance its energy dissipation and vibration behavior. Nowadays, the literature has many experimental findings that provide an interesting view of rubberized concrete's dynamic behavior. On the other hand, it still lacks research that collects, interprets, and numerically investigates these findings to provide some correlations and construct reliable prediction models for rubberized concrete's dynamic properties. Therefore, this study is intended to propose prediction approaches for the dynamic properties of rubberized concrete. As a part of the study, multiple linear regression and artificial neural networks will be used to create prediction models for dynamic modulus of elasticity, damping ratio, and natural frequency.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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• pp.81-89
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• 1992

After much research on qualitative reasoning, the problem of ambiguities still hampers the practicality of this important AI tool. In this paper, the sources of ambiguities are examined in depth with a systems engineering point of view and possible directions to disambiguation are suggested. This includes some modeling strategies and an architecture of temporal inference for building unambiguous qualitative models of practical complexity. It is argued that knowledge of multiple levels in abstraction hierarchy must be reflected in the modeling to resolve ambiguities by introducing the designer's decisions. The inference engine must be able to integrate two different types of temporal knowledge representation to determine the partial ordering of future events. As an independent quantity management system that supports the suggested modeling approach, LIQUIDS(Linear Quantity-Information Deriving System) is described. The inference scheme can be conjoined with ordinary rule-based reasoning systems and hence generalized into many different domains.