• Management & Information Systems Review
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• v.27
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• pp.71-99
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• 2008

This study investigates the relationships between three BSC communication attributes(support of organizational culture, message valid, and knowledge sharing) and organizational learning and how that translates into relationship organizational performance in public organization. In this paper, first, past studies on BSC communication and organizational learning that identify the attributes of effective communication and organizational learning in organizational performance are reviewed. Second, a research model, key variables, and three hypotheses tested by PLS(partial least squares) are presented. The data was collected from BSC champions and managers of 53 public organizations in Korea. The results indicate, first, BSC communication (except for support of organizational culture) have not significant related to organizational performance. Therefore, H1 was not supported. Second, the structural path coefficient between support of organizational culture and message valid and organizational learning are statistically significant and in the hypothesized direction. But the knowledge sharing has not significant relationship with organizational learning. Therefore, H2 was partially supported. Third, organizational learning was significantly positively related to organizational performance. H3 was supported. Finally, organizational learning play a significantly positive role in mediating the relationship between BSC communication and organizational performance. The theoretical contributions, limitations, as well as future research directions are discussed at the end of the paper.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.4
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• pp.506-520
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• 2019

This study first investigates relationships among fashion consumer's positive emotion toward social value seeking activities (SVSA), enterprise image (EI), enterprise-perceived quality (EPQ), and purchase intention. Additionally, it demonstrates if the confirmed relationships are different in similar SVSA between social enterprise and cause-related marketing (CRM). An online experiment using a 2 (the implementation organization of social values: social enterprise vs CRM) ${\times}2$ (SVSA: support of vulnerable group vs environmental protection) factorial design was conducted to test the established hypotheses. Participants were randomly assigned to one of four conditions, and the collected data were analyzed using a partial least squares structure equation modeling (PLS-SEM) and partial least squares multi-group analysis (PLS-MGA). The results revealed that positive emotion toward SVSA directly influenced EI and purchase intention. EI and EPQ were identified as sequential mediators linking positive emotion toward SVSA and purchase intention. A finding for similarity in consumer response paths between social enterprises and CRM highlights that social enterprises need to develop a marketing strategy distinguished from CRM.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1553-1556
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• 1997

In the paper, we porpose a new mehtod of extending PLS(Partial Least Squares) regressiion method to nonlinear framework and apply it to the estimation of product compositions in high-purity distillation column. There have veen similar efforets to overcome drawbacks of PLS by using nonlinear-mapping ability of meural networks, however, they failed to show great improvement over PLS since they focused only in capturing nonlinear functional relationship between input data, not on nonlinear correlation inthe data set. By incorporating the structure of Robust Auto Associative Networks(RAAN) into that of previous nonlinear PLS, we can handle nonlinear correlation as well as nonlinear functional relationship. The application result shows that the proposed method performs better than previous ones even for nonlinearities caused by changing operating conditions, limited observations, and existence of meas-unrement noises.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.236-242
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• 2015

We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.225-237
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• 2012

This study proposes a quantification algorithm for a PLS method with several sets of variables. We called the quantification method for PLS with more than 2 sets of data a generalization. The basis of the quantification for PLS method is singular value decomposition. To derive the form of singular value decomposition in the data with more than 2 sets more easily, we used the constraint, $a^ta+b^tb+c^tc=3$ not $a^ta=1$, $b^tb=1$, and $c^tc=1$, for instance, in the case of 3 data sets. However, to prove that there is no difference, we showed it by the use of 2 data sets case because it is very complicate to prove with 3 data sets. The keys of the study are how to form the singular value decomposition and how to get the coordinates for the plots of variables and observations.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.575-587
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• 2015

The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

• Journal of Ginseng Research
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• v.44 no.3
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• pp.413-423
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• 2020

Background: Ginseng berries (GBs) show temporal metabolic variations among different maturation stages, determining their organoleptic and functional properties. Methods: We analyzed metabolic variations concomitant to five different maturation stages of GBs including immature green (IG), mature green (MG), partially red (PR), fully red (FR), and overmature red (OR) using mass spectrometry (MS)-based metabolomic profiling and multivariate analyses. Results: The partial least squares discriminant analysis score plot based on gas chromatography-MS datasets highlighted metabolic disparity between preharvest (IG and MG) and harvest/postharvest (PR, FR, and OR) GB extracts along PLS1 (34.9%) with MG distinctly segregated across PLS2 (18.2%). Forty-three significantly discriminant primary metabolites were identified encompassing five developmental stages (variable importance in projection > 1.0, p < 0.05). Among them, most amino acids, organic acids, 5-C sugars, ethanolamines, purines, and palmitic acid were detected in preharvest GB extracts, whereas 6-C sugars, phenolic acid, and oleamide levels were distinctly higher during later maturation stages. Similarly, the partial least squares discriminant analysis based on liquid chromatography-MS datasets displayed preharvest and harvest/postharvest stages clustered across PLS1 (11.1 %); however, MG and PR were separated from IG, FR, and OR along PLS2 (5.6 %). Overall, 24 secondary metabolites were observed significantly discriminant (variable importance in projection > 1.0, p < 0.05), with most displaying higher relative abundance during preharvest stages excluding ginsenosides Rg1 and Re. Furthermore, we observed strong positive correlations between total flavonoid and phenolic metabolite contents in GB extracts and antioxidant activity. Conclusion: Comprehending the dynamic metabolic variations associated with GB maturation stages rationalize their optimal harvest time per se the related agroeconomic traits.

• The Journal of the Korea Contents Association
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• v.17 no.4
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• pp.247-255
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• 2017

The study figured out the preference factors which should be considered for investor on decision making of unsold apartment and analyzed by comparing the difference according to the type. Then, it investigated the preference factors through the previous studies to analyze the influence factor of decision making and demonstrated the effects through the PLS (Partial Least Squares) regression. In addition, it separated the target type to tenants and construction firms and carried out the survey for comparing the preference factors of investor type. The result of analysis found out that tenants emphasis on preference factors such as the internal factor (1.141), conditional relaxation (1.114), environment factor (1.107), social factor (1.048), external factor (1.030), educational environment factor (1.010) and etc. Then, construction firms emphasis on preference factors such as the social factor (1.401), environment factor (1.251), conditional relaxation (1.133) and etc. In addition, common preferences factors are the conditional relaxation, social factor, environment factor.

• Journal of the Korean Chemical Society
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• v.46 no.4
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• pp.309-317
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• 2002

A spectrofluorimetric method for the simultaneous determination of amino acids (tryptophan and tyrosine) based on the application of multivariate calibration method such as principal component regression and partial least squares (PLS) to luminescence measurements has been studied. Emission spectra of synthetic mixtures of two amino acids were obtained at excitation wavelength of 257 ㎚. The calibration model in PCR and PLS was obtained from the spectral data in the range of 280-500 ㎚ for each standard of a calibration set of 32 standards, each containing different amounts of two amino acids. The relative standard error of prediction ($RSEP_a$) was obtained to assess the model goodness in quantifying each analyte in a validation set. The overall relative standard error of prediction ($RSEP_m$) for the mixture obtained from the results of a validation set, formed by 6 independent mixtures was also used to validate the present method.

• Korean Chemical Engineering Research
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• v.45 no.6
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• pp.596-603
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• 2007

Rapid or on-line coal analysis is of great interest in coal industry as it would allow efficient plant operation. Multivariate analysis has been applied to near-infrared(NIR) spectra coal for investigating the relationship between coal properties(%) (moisture, ash, volatile matter, fixed carbon, carbon, hydrogen, nitrogen, oxygen, sulfur), heating value(kcal/kg) and corresponding near-infrared spectral data. The quantitative analysis was carried out by applying PLS(partial least squares regression) to determine a methodology able to establish a relationship between coal properties and NIR spectral data being applied mathematical pre-treatments for minimizing the physical features of the samples. As a results of the analysis, this technique is able to classify the species of coals and to predict the all coal properties except ash, nitrogen and sulfur. The efficient operation of coal fired power plant is expected owing to real time on-line coal analysis of moisture and heating value.