• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.229-237
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• 2024

The logistics industry is converging with digital technology and growing into various logistics automation systems. However, inspection and loading/unloading, which are mainly performed in logistics work, depend on human resources, and the workforce is shrinking due to the decline in the productive population due to the low birth rate and aging. Although much research is being conducted on the development of automated logistics systems to solve these problems, there is a lack of research and development on load stacking stability, which has the potential to cause significant accidents. In this study, loading boxes with various sizes and positions of the center of gravity were set up, and a method for stacking that with high stability is presented. The size of the loading box is measured using a depth camera. The loading box's weight and center of gravity are measured and estimated by a developed device with four loadcells. The measurement error is measured through various repeated experiments and is corrected using the least squares method. The robot arm performs load stacking by determining the target position so that the centers of gravity of the loading boxes with unbalanced masses with a random sequence are transported in alignment. All processes were automated, and the results were verified by experimentally confirming load stacking stability.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.3
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• pp.210-218
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• 2018

In order to generate meshes automatically for finite element analysis of complex structures such as aircraft, a large number of triangular elements are typically created. However, triangular elements are less accurate than rectangular elements, so it is difficult to obtain a reliable solution. This problem can be improved through the meshfree method using the back cell integration. However, this method also causes some problems such as over-use of the integration points and inefficiency of the integral domain. In order to improve these problems, a method of performing integration by setting the integral area based on a node basis has been proposed, but in the case of incompressible material problems, the numerical accuracy deteriorates due to the vibration phenomenon of the solution. Therefore, in this paper, the modified meshfree method is proposed which sets the integral domain as an element domain instead of the nodal domain, and the proposed method improves the numerical instability caused by the conventional meshfree method without decreasing the accuracy regardles of the shape of integral domain. The effectiveness of the modified meshfree method is verified by using 2-D examples.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.335-345
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• 2020

This paper deals with the problem of estimating the log-transformed linear regression model to fit actual battle data from the Ardennes Campaign of World War II into the Lanchester model. The problem of determining a global solution for parameters and multicollinearity problems are identified and modified by examining the results of previous studies on data. The least squares method requires attention because a local solution can be found rather than a global solution if considering a specific constraint or a limited candidate group. The method of exploring this multicollinearity problem can be confirmed by a statistic known as a variance inflation factor. Therefore, the Lanchester model is simplified to avoid these problems, and the combat power attrition rate model was proposed which is statistically significant and easy to explain. When fitting the model, the dependence problem between the data has occurred due to autocorrelation. Matters that might be underestimated or overestimated were resolved by the Cochrane-Orcutt method as well as guaranteeing independence and normality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.111-121
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• 2006

Recently, many studies have been performed to apply airborne LIDAR data to extracting urban models. In order to model efficiently the man-made objects which are the main components of these urban models, it is important to extract automatically planar patches from the set of the measured three-dimensional points. Although some research has been carried out for their automatic extraction, no method published yet is sufficiently satisfied in terms of the accuracy and completeness of the segmentation results and their computational efficiency. This study thus aimed to developing an efficient approach to automatic segmentation of planar patches from the three-dimensional points acquired by an airborne LIDAR system. The proposed method consists of establishing adjacency between three-dimensional points, grouping small number of points into seed patches, and growing the seed patches into surface patches. The core features of this method are to improve the segmentation results by employing the variable threshold value repeatedly updated through a statistical analysis during the patch growing process, and to achieve high computational efficiency using priority heaps and sequential least squares adjustment. The proposed method was applied to real LIDAR data to evaluate the performance. Using the proposed method, LIDAR data composed of huge number of three dimensional points can be converted into a set of surface patches which are more explicit and robust descriptions. This intermediate converting process can be effectively used to solve object recognition problems such as building extraction.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.4
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• pp.450-455
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• 2010

The study had two objectives, namely, to estimate the andrological disorders leading to disposal of Karan Fries (KF), Sahiwal cattle and Murrah buffalo bulls and to study the effect of various factors (species/breeds, season of birth and period of birth) on male reproductive parameters. Records on occurrence of subfertility problems and disposal pattern of bulls maintained at the National Dairy Research Institute herd were collected for 15 years (1991 to 2005). Percentage of bulls producing freezable semen was less in the crossbred cattle (58.46%) as compared to Sahiwal (81.69%) and Murrah bulls (81.05%). Various subfertility traits like poor libido and unacceptable seminal profile were found to be the significant reasons (p<0.01) for culling of the breeding bulls. Inadequate sex drive was the main contributing factor for bull disposal in Sahiwal (22.55%) and Murrah bulls (15.12%) whereas poor semen quality and freezability were most frequently observed in KF bulls (24.29 and 7.29 percent, respectively). Least squares analyses of different male reproductive parameters showed that species/breeds had significant effect (p<0.05) on all traits except for frozen semen production periods (FSPP). Periods of birth were significantly different (p<0.05) for all traits except for semen volume. Age at first semen collection (AFSC), age at first semen freezing (AFSF) and age at disposal (AD) were highest in Murrah, while frozen semen production period (FSPP) and semen production period (SPP) were highest in KF and lowest in Sahiwal. The age at first semen donation and breeding period could be reduced by introducing the bulls to training at an early age. These results revealed a declining trend in AFSC, AFSF, FSPP, SPP and AD, thereby indicating an improvement in reproductive performance over the years. The age at first semen donation in bovines can be reduced by introducing the young male calves to training at an early age, which could increase the dosage of semen obtained from each male.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1141-1141
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• 2001

The aim of the study was to evaluate the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural network (ANN) on the prediction of chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with information relative to moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected with 10 different Foss NIR Systems instruments, which were either standardized or not standardized to one master instrument. The spectra were trimmed to a wavelength range between 1100 and 2498 nm. Two data sets, one standardized (IVAL) and the other not standardized (SVAL) were used as independent validation sets, but 10% of both sets were omitted and kept for later expansion of the calibration database. The remaining samples were combined into one database (n=21,696), which was split into 75% calibration (CALBASE) and 25% validation (VALBASE). The chemical components in the 3 validation data sets were predicted with each model derived from CALBASE using the calibration database before and after it was expanded with 10% of the samples from IVAL and SVAL data sets. Calibration performance was evaluated using standard error of prediction corrected for bias (SEP(C)), bias, slope and R2. None of the models appeared to be consistently better across all validation sets. VALBASE was predicted well by all models, with smaller SEP(C) and bias values than for IVAL and SVAL. This was not surprising as VALBASE was selected from the calibration database and it had a sample population similar to CALBASE, whereas IVAL and SVAL were completely independent validation sets. In most cases, Local and ANN models, but not modified PLS, showed considerable improvement in the prediction of IVAL and SVAL after the calibration database had been expanded with the 10% samples of IVAL and SVAL reserved for calibration expansion. The effects of sample processing, instrument standardization and differences in reference procedure were partially confounded in the validation sets, so it was not possible to determine which factors were most important. Further work on the development of large databases must address the problems of standardization of instruments, harmonization and standardization of laboratory procedures and even more importantly, the definition of the database population.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.229-238
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• 2017

The study suggested plans to solve the problems of unsold apartments that lead to social and economic issues in our society, analyzed the characteristic factors related to the unsold apartment residents, investigated the preference factors, and finally determined the influence structure between the characteristic factors of residents and the mitigation factor of unsold apartment, for the repurchase intention. From previous studies, this study determined the characteristic factors based on the physical, environmental, social and economic characteristics to make up the characteristic factors for the unsold apartment residents. Then, through a Focus Group Interview (FGI) among the members of a real estate specialist group, it summarized the suitable factors for analysis and analyzed the influence structure between all factors through Partial Least Squares Structural Equation Modeling (PLS-SEM). The study results show that the mitigation factors are the most profound cause of the decision-making for unsold apartment residents, followed by economic factors and residential factors. In addition, the residential satisfaction for the unsold apartment residents has a positive relation with the repurchase intention.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.117-126
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• 2000

Conventional methods for collecting origin-destination trips have been mainly relied on the surveys of home or roadside interview. However, the methods tend to be costly, labor intensive and time disruptive to the trip makers, thus the methods are not considered suitable for Planning applications such as routing guidance, arterial management and information Provision, as the parts of deployments in Intelligent Transport Systems Motivated by the problems, more economic ways to estimate origin-destination trip tables have been studied since the late 1970s. Some of them, which have been estimating O-D table from link traffic counts are generally Entropy maximizing, Maximum likelihood, Generalized least squares(GLS), and Bayesian inference estimation etc. In the Paper, with user equilibrium constraint we formulate GLS problem for estimating O-D trips and develop a solution a1gorithm by using Genetic Algorithm, which has been known as a g1oba1 searching technique. For the purpose of evaluating the method, we apply it to Seoul inner ringroad and compare it with gradient method proposed by Spiess(1990). From the resu1ts we fond that the method developed in the Paper is superior to other.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.6
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• pp.649-656
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• 2011

A model for the numerical simulation of lithium-ion batteries (LIBs) is developed for use in battery cell design, with a view to improving the performances of such batteries. The model uses Newman-type electrochemical and transfer $theories^{(1,2)}$ to describe the behavior of the lithium-ion cell, together with the Levenberg-Marquardt optimization scheme to estimate the performance or design parameters in nonlinear problems. The mathematical model can provide an insight into the mechanism of LIB behavior during the charging/discharging process, and can therefore help to predict cell performance. Furthermore, by means of least-squares fitting to experimental discharge curves measured at room temperature, we were able to obtain the values of transport and kinetic parameters that are usually difficult to measure. By comparing the calculated data with the life-test discharge curves (SB LiMotive cell), we found that the capacity fade is strongly dependent on the decrease in the reaction area of active materials in the anode and cathode, as well as on the electrolyte diffusivity.