• Journal of the Korean Chemical Society
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• v.37 no.3
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• pp.344-350
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• 1993

25,26,27,28-Tetraacetoxycalix[4]arene·monohydrate is orthorhombic, space group Pbca with a = 14.979(4), b = 15.154(4), c = 27.890(3) ${\AA}$, Z = 8, V = 6330.6 ${\AA}^{-3}$, D$_c$ = 1.28 $g{\cdot}cm^{-3}$, (Mo K${\alpha}$) = 0.71069 ${\AA}$, ${\mu}$ = 0.86 cm$^{-1}$, F(000) = 2600, and R = 0.069 for 3376 unique observed reflections with I > 1.0 ${\sigma}$(I). The structure was solved by direct methods and refined by cascade diagonal least-squares refinement. All the C-H bond lengths(= 0.96 ${\AA}$), the methyl groups and the methylene groups are fixed and refined as the rigid groups with ideal geometry. The macrocycle exists in the 1,3 alternate conformation (by Conforth) making the angles of 110.7, 684, 113.7 and 68.8$^{\circ}$ between the benzene rings and the methylenic mean plane, and four each acetoxy groups are twisted away from their own benzene rings with the angles of 68.2, 97.6, 78.9 and 71.3$^{\circ}$, respectively. The relative dihedral angles between two opposite side of the benzene rings are 135.6$^{\circ}$ for the rings (1) and (3) and 135.2$^{\circ}$ for (2) and (4). A water molecule which has nearly the same height of the methylenic plane of the macrocycle in the c-axis, is located within the distances of 2.942(5) ${\AA}$ from the O(8) atom of the carbonyl group and 2.901 ${\AA}$ from, another O(2)(1/2-x, -1/2＋y, z). The shortest contact between the molecule is 3.193 ${\AA}$ from the O(4) to the C(3)(1/2＋x, 1/2-y,-z).

• Journal of The Korean Society of Grassland and Forage Science
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• v.32 no.3
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• pp.301-308
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• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of Italian ryegrass silages. A population of 267 Italian ryegrass silages representing a wide range in chemical parameters and fermentative characteristics was used in this investigation. Samples of silage were scanned at 2 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of the highest coefficients of determination in cross validation ($R^2$) and the lowest standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The $R^2$ and SECV were 0.98 (SECV 1.27%) for moisture, 0.88 (SECV 1.26%) for ADF, 0.84 (SECV 2.0%), 0.93 (SECV 0.96%) for CP and 0.78 (SECV 0.56), 0.81 (SECV 0.31%), 0.88 (SECV 1.26%) and 0.82 (SECV 4.46) for pH, lactic acid, TDN and RFV on a dry matter (%), respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation quality of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Journal of the Mineralogical Society of Korea
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• v.6 no.1
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• pp.1-16
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• 1993

The chemical and optical absorption spectroscopic characters of pink and colorless tourmalines from San Diego mine in California, U.S.A., blue/green tourmalines from anonymous mine, Brazil, and brownis black tourmalines from Uncheon and Haksan mines in Korea have been studied using X-ray diffractometer, electron microprobe, optical absorption spectroscopy, and heat treatment. Least-squares refinements give unit cell diminsions : a = 15.96-16.01 ${\AA}$, c = 7.15-7.16 ${\AA}$ for the brownish black tourmalines, a = 15.82 - 15.87 ${\AA}$, c = 7.09 - 7.10 ${\AA}$ for pink tourmalines, and a = 15.88 - 15.94 ${\AA}$, c = 7.12 - 7.15 ${\AA}$ for blue green tourmalines. The colors of tourmalines are responsible for the transition elements. The pink color is attributed to the $Mn^{3+}$ ions, the blue-green to $Fe^{2+}$ and $Mn^{2+}$, bluish green to $Cu^{2+}$, and the brownish black to $Fe^{2+}$, $Fe^{2+}$ - $Fe^{3+}$, and $Fe^{2+}$ - $Ti^{4+}$. The $Mn^{3+}$ ions of pink color tourmalines are stabilized in the Y sites compressed along the O(1)H-O(3)H axis by Jahn-Teller distortion. Heating removes the pink or red component from tourmalines, producing the colorless stones from the pink and red ones. The bluish green samples change into the greenish blue ones and a certain yellowish green samples change into the light green ones by heat treatment. In the elbaite-schorl series, the concentration of Fe and Mn are variable depending on the color zones. The green zone is characterrized by the high content of Fe and Mn are variable depending on the color zones. The green zone is characterized by the high content of Fe, whereas the pink zone by the high content of Mn. Mn increases in deep yellow zone compared with yellow or colorless zones.

• KDI Journal of Economic Policy
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• v.12 no.2
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• pp.51-79
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• 1990

Research on technical efficiency, an important dimension of market performance, had received little attention until recently by most industrial organization empiricists, the reason being that traditional microeconomic theory simply assumed away any form of inefficiency in production. Recently, however, an increasing number of research efforts have been conducted to answer questions such as: To what extent do technical ineffciencies exist in the production activities of firms and plants? What are the factors accounting for the level of inefficiency found and those explaining the interindustry difference in technical inefficiency? Are there any significant international differences in the levels of technical efficiency and, if so, how can we reconcile these results with the observed pattern of international trade, etc? As the first in a series of studies on the technical efficiency of Korea's manufacturing industries, this paper attempts to answer some of these questions. Since the estimation of technical efficiency requires the use of plant-level data for each of the five-digit KSIC industries available from the Census of Manufactures, one may consture the findings of this paper as empirical evidence of technical efficiency in Korea's manufacturing industries at the most disaggregated level. We start by clarifying the relationship among the various concepts of efficiency-allocative effciency, factor-price efficiency, technical efficiency, Leibenstein's X-efficiency, and scale efficiency. It then becomes clear that unless certain ceteris paribus assumptions are satisfied, our estimates of technical inefficiency are in fact related to factor price inefficiency as well. The empirical model employed is, what is called, a stochastic frontier production function which divides the stochastic term into two different components-one with a symmetric distribution for pure white noise and the other for technical inefficiency with an asymmetric distribution. A translog production function is assumed for the functional relationship between inputs and output, and was estimated by the corrected ordinary least squares method. The second and third sample moments of the regression residuals are then used to yield estimates of four different types of measures for technical (in) efficiency. The entire range of manufacturing industries can be divided into two groups, depending on whether or not the distribution of estimated regression residuals allows a successful estimation of technical efficiency. The regression equation employing value added as the dependent variable gives a greater number of "successful" industries than the one using gross output. The correlation among estimates of the different measures of efficiency appears to be high, while the estimates of efficiency based on different regression equations seem almost uncorrelated. Thus, in the subsequent analysis of the determinants of interindustry variations in technical efficiency, the choice of the regression equation in the previous stage will affect the outcome significantly.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.73-91
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• 2013

Advertising market has been facing new challenges due to dramatic change in advertising channels and the advent of innovative media such as mobile devices. Recent research related to mobile devices is mainly focused on the fact that mobile devices could identify users'physical location in real-time, and this sheds light on how location-based technology is utilized to achieve competitive advantage in advertising market. With the introduction of smartphone, the functionality of smartphone has become much more diverse and context awareness is one of the areas that require further study. This work analyses the influence of context awareness value resulted from the transformation of advertising channel in mobile communication market, and our research result reflects recent trend in advertising market environment which is not considered in previous studies. Many constructs has intensively been studied in the context of advertising channel in traditional marketing environment, and entertainment, irritation and information are considered to be the most widely accepted variables that has positive relationship with advertising value. Also, in smartphone advertisement, four main dimensions of context awareness value are recognized: identification, activity, timing and location. In this study, we assume that these four constructs has positive relationship with context awareness value. Finally, we propose that advertising value and context awareness value positively influence smartphone advertising attitude. Partial Least Squares (PLS) structural model is used in our theoretical research model to test proposed hypotheses. A well designed survey is conducted for college students in Korea, and reliability, convergent validity and discriminant validity of constructs and measurement indicators are carefully evaluated and the results show that reliability and validity are confirmed according to predefined statistical criteria. Goodness-of-fit of our research model is also supported. In summary, the results collectively suggest good measurement properties for the proposed research model. The research outcomes are as follows. First, information has positive impact on advertising value while entertainment and irritation have no significant impact. Information, entertainment and irritation together account for 38.8% of advertising value. Second, along with the change in advertising market due to the advent of smartphone, activity, timing and location have positive impact on context awareness value while identification has no significant impact. In addition, identification, activity, location and time together account for 46.3% of context awareness value. Third, advertising value and context awareness value both positively influence smartphone advertising attitude, and these two constructs explain 31.7% of the variability of smartphone advertising attitude. The theoretical implication of our research is as follows. First, the influence of entertainment and irritation is reduced which are known to be crucial factors according to previous studies related to advertising value, while the influence of information is increased. It indicates that smartphone users are not likely interested in entertaining effect of smartphone advertisement, and are insensitive to the inconvenience due to smartphone advertisement. Second, in today' ubiquitous computing environment, it is effective to provide differentiated advertising service by utilizing smartphone users'context awareness values such as identification, activity, timing and location in order to achieve competitive business advantage in advertising market. For practical implications, enterprises should provide valuable and useful information that might attract smartphone users by adopting differentiation strategy as smartphone users are sensitive to the information provided via smartphone. Also enterprises not only provide useful information but also recognize and utilize smarphone users' unique characteristics and behaviors by increasing context awareness values. In summary, our result implies that smartphone advertisement should be optimized by considering the needed information of smartphone users in order to maximize advertisement effect.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.

• Asia pacific journal of information systems
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• v.23 no.4
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• pp.1-28
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• 2013

Given that smartphone-based social network services (SNS), such as KakaoStory is now being widely used as a way for people to connect and communicate with each other, this study examines key factors leading to the continued use of SNS. People have been using PC-based SNS, such as Cyworld, for years are now using smartphone-based SNS, such as KakaoStory. KakaoStory developed by KakaoTalk has rapidly grown up as the largest smartphone-based SNS in Korea as smartphone penetration increases. It is more difficult for firms to maintain their current users over time in that alternative SNSs satisfying people's new needs are constantly emerging and evolving. In this sense, one of the most challenging issues for SNS firms is how to retain their current users in the long run. However, there are few empirical studies on this issue. Applying the IS continuance model proposed by Bhattacherjee [2001], this study explores key determinants of users' smartphone-based SNS continuance intention. The model suggests that perceived usefulness and user satisfaction are the key determinants of IS continuance intention. However, the model includes only the utilitarian value that users can obtain through the use of smartphone-based SNS, by considering perceived usefulness. Therefore, the study attempts to extend the IS continuance model by considering hedonic and social values simultaneously. More specifically, we consider subjective norms as social value that are proposed by the theory of reasoned action and the theory of planned behavior. We also consider perceived enjoyment as hedonic value that is emphasized as a key factor influencing users' behavior intention and actual behavior, particularly in the context of hedonic IS use. By considering the three values in our model simultaneously, we could offer a deeper understanding of smartphone-based SNS continuance. That is, this study could offer an explanation of how each value is associated with user satisfaction and SNS continuance intention. The customer value-satisfaction-loyalty model can strengthen the assertion that smartphone-based SNS continuance intention is determined by various different types of customer values, such as utilitarian, hedonic, and social ones. Moreover, the model provides a theoretical basis for the assertion that customer values lead to increased loyalty via customer satisfaction. In this regard, we theorize that SNS continuance intention is influenced by users' various values, namely perceived usefulness, perceived enjoyment, and subjective norms, via user satisfaction. To test the proposed research model and hypotheses, we conducted a partial least squares analysis using a total of 253 data collected on the users of smartphone-based SNS (i.e., KakaoStory). The key findings are as follows: First, it has been found that SNS continuance intention considerably depends on user satisfaction. Second, user satisfaction is determined by confirmation, perceived usefulness, and perceived enjoyment. Third, concerning the effects of the three values on SNS continuance intention, only perceived enjoyment regarded as hedonic value was statistically significant. That is, perceived usefulness considered as utilitarian value and subjective norms considered as social value had no effect on SNS continuance intention. Finally, our results indicate that confirmation increases perceived usefulness, perceived enjoyment, and user satisfaction. The results reconfirm the effectiveness of IS continuance model in predicting smartphone-based SNS continuance intention. Moreover, the results demonstrate that perceived enjoyment reflecting hedonic value is the most important predictor of SNS continuance intention. Therefore, it is imperative for firms to meet SNS users' hedonic value to retain them in the long run. Meanwhile, we could not find any empirical evidence to support the assertion that subjective norms are associated with user satisfaction and SNS continuance intention. The results lead us to conclude that when users have enough direct experience in SNS use, subjective norms have no effect on SNS continuance intention. Discussions and implications of the results are provided.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.1
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• pp.50-57
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• 2016

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation ($R^2{_{cv}}$) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, $R^2{_{cv}}$, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy ($R^2{_{cv}}$ 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.

• Food Engineering Progress
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• v.15 no.1
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• pp.48-55
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• 2011

In this research, the near-infrared absorption from 1,100-2,300 nm was used to measure the content of capsaicinoids in the red-pepper powder by using the Acousto-optic tunable filters (AOTF) spectrometer with sample plate and sample rotating unit. Non-spicy red-pepper samples from one location (Younggwang-gun. Korea) were mixed with spicy one (var. Chungyang) to make samples separated by particle size (below 0.425 mm, 0.425-0.71 mm, and 0.71- 1.4 mm). The Partial Least Squares Regression (PLSR) model to predict the capsaicinoid content on particle sizes was developed with measured spectra by AOTF spectrometer and used to analyze the amount of capsaicinoids by HPLC. The PLSR Model of red-pepper powder of below 0.425 mm, 0.425-0.71 mm, and 0.71-1.4 mm with cross validation had ${R_V}^2$ = 0.948-0.979 and Standard Error of Prediction (SEP) = 6.56-7.94 mg%. The prediction error of smaller particle size of red-pepper powder was low. The best PLSR model was found in pretreatment of Range Normalization, Standard Normal Variate, and 1st Derivatives of red-pepper powder of below 1.4 mm with cross validation, having ${R_V}^2$ = 0.959 and SEP = 8.82 mg%.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.2
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• pp.114-120
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• 2019

This study was carried out to explore the accuracy of near infrared spectroscopy(NIRS) for the prediction of moisture content and chemical parameters on winter annual forage crops. A population of 2454 winter annual forages representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation($R^2$) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS calibration model to predict the moisture contents and chemical parameters had very high degree of accuracy except for barely. The $R^2$ and SECV for integrated winter annual forages calibration were 0.99(SECV 1.59%) for moisture, 0.89(SECV 1.15%) for acid detergent fiber, 0.86(SECV 1.43%) for neutral detergent fiber, 0.93(SECV 0.61%) for crude protein, 0.90(SECV 0.45%) for crude ash, and 0.82(SECV 3.76%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of winter annual forage for routine analysis method to evaluate the feed value.