• Journal of Distribution Science
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• v.12 no.2
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.1
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• pp.17-26
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• 2008

The 1st order level network of Korea was adjusted simultaneously in 1987. After that, the 1 st order level network of Korea was adjusted simultaneously by National Geographic Information Institute in 2006. The levelling data were acquired by digital level with invar staff from 2001 through 2006. The 1st order level network consists of 36 level lines. Among them, 34 level lines comprise 11 level loops. Among 36 level lines, 4 level lines have fore & back error larger than the regulations for the 1st order levelling of NGII, Korea. Also, the closing error of 3 loops of level network exceed the regulation for the 1st order levelling of NGII. The standard error of fore and back leveling between bench marks(${\eta}_1$) are distributed between 0.2 $mm/{\surd}km$ and 1.7 $mm/{\surd}km$. The standard error of loop closing(${\eta}_2$) is 2.0 $mm/{\surd}km$. This result means that the 1st order level network of Korea qualifies for the high precision leveling defined by International Geodetic Association in 1948. As the result of the 1st order level network adjustment, the reference standard error($\hat{{\sigma}_0}$) of the level network was 1.8 $mm/{\surd}km$, which is twice as good as that of the 1st adjustment of level networks in 1987.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.5
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• pp.632-637
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• 2011

A new tooth profile which is adjusted on the amount of addendum modification factor is proposed for reducing vibration and noise of gears. The transmission error of the new profile can be designed more uniformly than that of the standard involute profile. The basic concepts of tooth profile modification are to reduce the load in contact area and to find the appropriate profile modification factor for operation condition. In this study, gears were estimated to constructive safety of bending strength and contact strength durability by using ROMAX program, and were compared with results by design formula of AGMA standard.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.7-10
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• 2007

The 1st order vertical network of Korea was adjusted in 1987 at first time. This is the second adjustment of the 1st order vertical network of Korea by National Geographic Information Institute. All the levelling data were acquired by digital level with invar staff. The number of 1st order level lines are 36, and 34 level lines comprise 11 circles of level network. Backward and forward error of a few level lines are larger than the regulations of NGII, Korea. Also, 3 circles of vertical network has circuit closure error that is exceed the regulation. As the result of 1st order vertical network adjustment, the reference standard error of the vertical network was $1.8mm/{\surd}km$.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.114-125
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• 2003

This paper proposes multivariate process capability indices (PCIs) for skewed populations using $T^2$rand modified process region approaches. The proposed methods are based on the multivariate version of a weighted standard deviation method which adjusts the variance-covariance matrix of quality characteristics and approximates the probability density function using several multivariate Journal distributions with the adjusted variance-covariance matrix. Performance of the proposed PCIs is investigated using Monte Carlo simulation, and finite sample properties of the estimators are studied by means of relative bias and mean square error.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.105-110
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• 1998

In this study, statistical and neural network methods were used to recognize the cutting tool states. This system employed the tool dynamometer and cutting force signals which are processed from the tool dynamometer sensor using linear discriminent function. To learn the necessary input/output mapping for turning operation diagnosis, the weights and thresholds of the neural network were adjusted according to the error back propagation method during off-line training. The cutting conditions, cutting force ratios and statistical values(standard deviation, coefficient of variation) attained from the cutting force signals were used as the inputs to the neural network. Through the suggested neural network a cutting tool states may be successfully diagnosed.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1511-1511
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• 2001

Energy content, expressed as calories per gram, is an important part of the evaluation and marketing of foods in developed countries. Currently accepted methods of measurement of energy by U.S. food labeling legislation include measurement of gross calories by bomb calorimetry with an adjustment for undigested protein and by calculation using specific factors for the energy values of protein, carbohydrate less the amount of insoluble dietary fiber, and total fat. The ability of NIRS to predict the energy value of diverse, processed and unprocessed cereal food products was investigated. NIR spectra of cereal products were obtained with an NIR Systems monochromator and the wavelength range used for analysis was 1104-2494 nm. Gross energy of the foods was measured by oxygen bomb calorimetry (Parr Manual No. 120) and expressed as calories per gram (CPGI, range 4.05-5.49 cal/g). Energy value was adjusted for undigested protein (CPG2, range 3.99-5.38 cal/g) and undigested protein and insoluble dietary fiber (CPG3, range 2.42-5.35 cal/g). Using a multivariate analysis software package (ISI International, Inc.) partial least squares models were developed for the prediction of energy content. The standard error of cross validation and multiple coefficient of determination for CPGI using modified partial least squares regression (n=127) was 0.060 cal/g and 0.95, respectively, and the standard error of performance, coefficient of determination, bias and slope using an independent validation set (n=59) were 0.057 cal/g, 0.98, -0.027 cal/g and 1.05 respectively. The PLS loading for factor 1 (Pearson correlation coefficient 0.92) had significant absorption peaks correlated to C-H stretch groups in lipid at 1722/1764 nm and 2304/2346 nm and O-H groups in carbohydrate at 1434 and 2076 nm. Thus the model appeared to be predominantly influenced by lipid and carbohydrate. Models for CPG2 and CPG3 showed similar trends with standard errors of performance, using the independent validation set, of 0.058 and 0.088 cal/g, respectively, and coefficients of determination of 0.96. Thus NIRS provides a rapid and efficient method of predicting energy content of diverse cereal foods.

• Journal of Periodontal and Implant Science
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• v.30 no.2
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• pp.429-442
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• 2000

Based on current evidence in the literature, it is known that endotoxin is a weakly adherent surface phenomenon and that power-driven instruments can be used to accomplish definitive root detoxification and maximal wound healing without overinstrumentation of root and without extensive cementum removal. And one of the newly developed curette tips used with low power of piezoelectric ultrasonic scaler, is effective to remove calculus and not to remove the excessive cementum. The purpose of this study is therefore, to assess the influence of ultrasonic power and various working parameters on root substitute removal when instrumentation is performed with the curette tip on piezoelectric ultrasonic scaler. This study assessed defect depth, width and area resulting from instrumentation using a piezoelectric ultrasonic scaler with a curette type tip in vitro to acrylic resin block as a root substitute. The working parameters was standardized by the sledge device which controls lateral force(0.5 N, 1 N, 2 N) and instrumentation time(5 sec, 10 sec, 20 sec) and power setting was adjusted 0,2,4,8 in P mode. Power setting had the greatest influence on defect depth compared to lateral force and instrumentation time(standardized regression parameter estimates${\pm}$standard error, $0.37{\pm}0.02$, $0.19{\pm}0.02$, $0.07{\pm}0.02$). The effects on defect area also greatest for power setting($0.57{\pm}0.03$) compared to lateral force and instrumentation time($0.33{\pm}0.03$, $0.12{\pm}0.03$). The effect of the power setting on the defect width($0.15{\pm}0.01$) is not so great as defect depth or defect area compared to lateral force($0.12{\pm}0.01$) and effect of instrumentation time is minimal($0.02{\pm}0.01$). It could be concluded that the power setting has the greatest influence on the defect depth and area in curette type tip with low power of piezoelectric ultrasonic device. Many parameters can be adjusted in various situation in clinical use of piezoelectric ultrasonic scaler but the power setting is the first parameter to be adjusted.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.26-38
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• 2022

This paper proposes multiple RLS and actuator performance index-based adaptive actuator fault-tolerant control and detection algorithms for longitudinal autonomous driving. The proposed algorithm computes the desired acceleration using feedback law for longitudinal autonomous driving. When actuator fault or performance degradation exists, it is designed that the desired acceleration is adjusted with the calculated feedback gains based on multiple RLS and gradient descent method for fault-tolerant control. In order to define the performance index, the error between the desired and actual accelerations is used. The window-based weighted error standard deviation is computed with the design parameters. Fault level decision algorithm that can represent three fault levels such as normal, warning, emergency levels is proposed in this study. Performance evaluation under various driving scenarios with actuator fault was conducted based on co-simulation of Matlab/Simulink and commercial software (CarMaker).

• Journal of Korean Society of Forest Science
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• v.77 no.1
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• pp.23-31
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• 1988

A total of 320 Larix leptocepzs Trees growing in Kyonggi and Kangwon Province were examined to estimate tree volume and height with DBH and age. The criteria of selecting the "best" regression equation were based on coefficient of determination, $R^2$, adjusted standard error, I, and the ease of application. From the results obtained in this study, the method of estimating tree volume and height with DBH and age showed the following advantages ; 1. Since no measurement of tree height is needed, it saves time and labor in forest survey. 2. It has the ease of application because age is determined directly in artificial forests. 3. Volume and height can be estimated more easily and accurately with DBH and age than other methods.