• The Journal of Society for e-Business Studies
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• v.7 no.1
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• pp.87-106
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• 2002

The behavioral and dynamic implications of an ERP implementation/installation are, to say the least, not well understood. Getting the switches set to enable the ERP software to go live is becoming straightforward. The really difficult part is understanding all of the dynamic interactions that accrue as a consequence. Dynamic causal and connectionist models are employed to facilitate an understanding of the dynamics and to enable control of the information-enhanced processes to take place. The connectionist model ran be analyzing (behind the scenes) the information accesses and transfers and coming If some conclusions about strong linkages that are getting established and what the behavioral implications of those new linkages and information accesses we. Ultimately, the connectionist model will come to an understanding of the dynamic, behavioral implications of the larger ERP implementation/installation per se. The underlying connectionist model will determine information transfers and workflow. Once a map of these two infrastructures is determined by the model, it becomes a relatively easy job for an analyst to suggest improvements in both. Connectionist models start with analog object structures and then use learning to produce mechanisms for managerial problem diagnoses. These mechanisms are neural models with multiple-layer structures that support continuous input/output. Based on earlier work performed and published by the author[10][11], a Connectionist ReasOning and LEarning System(CROLES) is developed that mimics the real-world reasoning infrastructure. Coupled with an explanation subsystem, this system can provide explanations as to why a particular reasoning structure behaved the way it did. Such a system operates in the backgmund, observing what is happening as every information access, every information response coming from each and every intelligent node (whether natural or artificial) operating within the ERP infrastructure is recorded and encoded. The CROLES is also able to transfer all workflows and map these onto the decision-making nodes of the organization.

• Health Policy and Management
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• v.9 no.2
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• pp.95-117
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• 1999

The purposes of the study was to analysis the factors on the physicians' indemnity experience and indemnity on malpractice. Data was collected from mail interview for the physicians from August, to October in 1996. Questions were asked to the physician who selected with random sample(n=8.338) about the opinion of malpractice insurance. experience that he(she) have requested the indemnity from patience. context of experienced indemnity and demographic characteristics of physician and patience. Response rate is 37.5%(n=3,124). This study was analyzed in two levels' the first. influential factors on whether physician has experience of indemnity and the second. influential factors of indemnity among physicians who had experienced the indemnity. The major findings were as follows : 1. Logistic regression on whether physicians had experience of indemnity request was conducted. And it indicated that statistically meaningful variables of model 1 (about all physicians) were department of surgery, physicians who have intention of insurance fee, physician age and income, physicians who owned the hospitals and statistically meaningful variables of model 11 (about physicians who owned the hospital) were department of surgery and internal treatment. 2. Multiple regression on the influential factors on indemnity was conducted. And it showed that statistically meaningful variables in model 1 were method of malpractice quarrel(physician association), whether physician had malpractice, whether suit succeeded, physician age, average practice time and income and whether physician owned the hospital and statistically meaningful variables of model 11 were whether physician had malpractice, number of outpatient, number of beds. As the conclusion, the thesis was examined about the variables related with experience of indemnity and cost of malpractice. But in order to prevent malpractice and promote medical quality, the reasonable system to solve a malpractice have to settle and cost estimation on malpractice is essential. Therefore an advanced research is progressed with methodology to decide the indemnity bases.

• Advances in concrete construction
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• v.13 no.5
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• pp.385-394
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• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.31-39
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• 2023

The evolution of Natural Language Processing (NLP) and the rise of large language models (LLM) like ChatGPT have paved the way for specialized question-answering (QA) systems tailored to specific domains. This study outlines a system harnessing the power of LLM in conjunction with document search algorithms to interpret and address user inquiries using documents from the Korea Atomic Energy Research Institute (KAERI). Initially, the system refines multiple documents for optimized search and analysis, breaking the content into managable paragraphs suitable for the language model's processing. Each paragraph's content is converted into a vector via an embedding model and archived in a database. Upon receiving a user query, the system matches the extracted vectors from the question with the stored vectors, pinpointing the most pertinent content. The chosen paragraphs, combined with the user's query, are then processed by the language generation model to formulate a response. Tests encompassing a spectrum of questions verified the system's proficiency in discerning question intent, understanding diverse documents, and delivering rapid and precise answers.

• Journal of Korean Society on Water Environment
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• v.40 no.3
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• pp.121-129
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• 2024

Harmful cyanobacterial blooms (HCBs) are caused by the rapid proliferation of cyanobacteria and are believed to be exacerbated by climate change. However, the extent to which HCBs will be stimulated in the future due to increased temperature remains uncertain. This study aims to predict the future occurrence of cyanobacteria in the Nakdong River, which has the highest incidence of HCBs in South Korea, based on temperature rise scenarios. Representative Concentration Pathways (RCPs) were used as the basis for these scenarios. Data-driven model simulations were conducted, and out of the four machine learning techniques tested (multiple linear regression, support vector regressor, decision tree, and random forest), the random forest model was selected for its relatively high prediction accuracy. The random forest model was used to predict the occurrence of cyanobacteria. The results of boxplot and time-series analyses showed that under the worst-case scenario (RCP8.5 (2100)), where temperature increases significantly, cyanobacterial abundance across all study areas was greatly stimulated. The study also found that the frequencies of HCB occurrences exceeding certain thresholds (100,000 and 1,000,000 cells/mL) increased under both the best-case scenario (RCP2.6 (2050)) and worst-case scenario (RCP8.5 (2100)). These findings suggest that the frequency of HCB occurrences surpassing a certain threshold level can serve as a useful diagnostic indicator of vulnerability to temperature increases caused by climate change. Additionally, this study highlights that water bodies currently susceptible to HCBs are likely to become even more vulnerable with climate change compared to those that are currently less susceptible.

• Journal of Korean Society of Archives and Records Management
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• v.20 no.1
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• pp.115-137
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• 2020

This study aims to examine the limitations of status that describe archives based on the Archival rules, and to propose a new method using the Records in Context - Conceptual model (RiC-CM) as a solution. Given this, the study conducted literature reviews and case studies. The solutions based on RiC-CM and its effects on the limitations of the existing environment are as follows. First, RiC-CM can describe multiple provenances about archives. This can be solved by defining individual records and provenances as "entity" and expressing their associations as relationships. The interrelation of entities alone can more accurately represent the information of provenances associated with a particular archive, making it easier to identify the overall context that makes records. Second, RiC-CM can link related files. Those that belong to a specific records group (fonds) can be resolved by assigning them to individual entities and making interrelation according to the context that makes records. This method makes it possible to serve information about the context that makes records. From the user's point of view, more options are available for searching records. Third, RiC-CM can link all relevant producer-made records related to a specific production organization. If organizations are related to each other, they can be defined as "entity," and their relationship can be expressed as "associated with." It helps to comprehensively examine the context of provenances. The findings of this study are expected to be used as a basis for future research on RiC-CM, in response to the paradigm shift for electronic records management systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.317-326
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• 2024

Biometric recognition is a technology that determines whether a person is identified by extracting information on a person's biometric and behavioral characteristics with a specific device. Cyber threats such as forgery, duplication, and hacking of biometric characteristics are increasing in the field of biometrics. In response, the security system is strengthened and complex, and it is becoming difficult for individuals to use. To this end, multiple biometric models are being studied. Existing studies have suggested feature fusion methods, but comparisons between feature fusion methods are insufficient. Therefore, in this paper, we compared and evaluated the fusion method of multiple biometric models using fingerprint, face, and iris images. VGG-16, ResNet-50, EfficientNet-B1, EfficientNet-B4, EfficientNet-B7, and Inception-v3 were used for feature extraction, and the fusion methods of 'Sensor-Level', 'Feature-Level', 'Score-Level', and 'Rank-Level' were compared and evaluated for feature fusion. As a result of the comparative evaluation, the EfficientNet-B7 model showed 98.51% accuracy and high stability in the 'Feature-Level' fusion method. However, because the EfficietnNet-B7 model is large in size, model lightweight studies are needed for biocharacteristic fusion.

• Laboraroty Animal Research
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• v.34 no.4
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• pp.248-256
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• 2018

O-2-$^{18}F$-fluoroethyl-l-tyrosine ($[^{18}F]FET$) has been widely used for glioblastomas (GBM) in clinical practice, although evaluation of its applicability in non-clinical research is still lacking. The objective of this study was to examine the value of $[^{18}F]FET$ for treatment evaluation and prognosis prediction of anti-angiogenic drug in an orthotopic mouse model of GBM. Human U87MG cells were implanted into nude mice and then bevacizumab, a representative anti-angiogenic drug, was administered. We monitored the effect of anti-angiogenic agents using multiple imaging modalities, including bioluminescence imaging (BLI), magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET/CT). Among these imaging methods analyzed, only $[^{18}F]FET$ uptake showed a statistically significant decrease in the treatment group compared to the control group (P=0.02 and P=0.03 at 5 and 20 mg/kg, respectively). This indicates that $[^{18}F]FET$ PET is a sensitive method to monitor the response of GBM bearing mice to anti-angiogenic drug. Moreover, $[^{18}F]FET$ uptake was confirmed to be a significant parameter for predicting the prognosis of anti-angiogenic drug (P=0.041 and P=0.007, on Days 7 and 12, respectively, on Pearson's correlation; P=0.048 and P=0.030, on Days 7 and 12, respectively, on Cox regression analysis). However, results of BLI or MRI were not significantly associated with survival time. In conclusion, this study suggests that $[^{18}F]FET$ PET imaging is a pertinent imaging modality for sensitive monitoring and accurate prediction of treatment response to anti-angiogenic agents in an orthotopic model of GBM.

• Journal of the Korean Society of Costume
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• v.64 no.2
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• pp.35-49
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• 2014

In cases of fashion businesses operating by consignment, base estimate on quantity of sales is the most essential part of merchandising. This study classified factors influential to sales into factors with systematic influence and factors with unsystematic influence. In order to find out influence of each factor on sales, non-linear regression was used with SPSS package on the basis of actual data on sales for 5 years for sport shoes brand. Major findings of this study are as follows. First, price level had significant negative(-) influence on sales. Second, price expectation effects had significant negative(-) influence on sales. Third, competitor's price effect showed significant negative(-) value. Fourth, day-of-the-week effect showed significant positive(+) effect. The theoretical marketing implications of this study are as follows. First, study on price leads to expansion of the researches from apparels to sport shoes. Field of study on price was enlarged through expansion of variable of study from price level and price expectation effect to promotion, day-of-the-week effect and rainfall effect. Second, quantitative scale of day-of-the-week effect was found and it could be confirmed that there was seasonal differences with day-of-the-week effect. Implications of above findings on marketing managers are as follows. First, it was found that an increase in competitiveness of brand power and a decline in absolute value of competitor's price effect can be realized when new product groups are developed to meet the unsatisfied needs in the market. Second, it was possible to find out the parameters scales of the price response function, making it possible to estimate sales for the next season, and in turn realize increase in rate of sales and profit rate. This research is based on the dynamic price response function, which is rare to find in the apparel business and it academic significance due to its expanding response model which was focused on price in conventional researches to non-systematic variables.

• Korean Journal of Agricultural Science
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• v.7 no.2
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• pp.131-144
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• 1980

The Linear Regression Model to extend the monthly runoff data in the short-recorded river was proposed by the author in 1979. Here in this study generalization precedure is made to apply that model to any given river basin and to any given station. Lengthier monthly runoff data generated by this generalized model would be useful for water resources assessment and waterworks planning. The results are as follows. 1. This Linear Regression Model which is a transformed water-balance equation attempts to represent the physical properties of the parameters and the time and space varient system in catchment response lumpedly, qualitatively and deductively through the regression coefficients as component grey box, whereas deterministic model deals the foregoings distributedly, quantitatively and inductively through all the integrated processes in the catchment response. This Linear Regression Model would be termed "Statistically deterministic model". 2. Linear regression equations are obtained at four hydrostation in Geum-river basin. Significance test of equations is carried out according to the statistical criterion and shows "Highly" It is recognized th at the regression coefficients of each parameter vary regularly with catchment area increase. Those are: The larger the catchment area, the bigger the loss of precipitation due to interception and detention storage in crease. The larger the catchment area, the bigger the release of baseflow due to catchment slope decrease and storage capacity increase. The larger the catchment area, the bigger the loss of evapotranspiration due to more naked coverage and soil properties. These facts coincide well with hydrological commonsenses. 3. Generalized diagram of regression coefficients is made to follow those commonsenses. By this diagram, Linear Regression Model would be set up for a given river basin and for a given station (Fig.10).