• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.2
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• pp.167-173
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• 2016

This study investigated the effects of green tea polyphenol and vitamin C on type 2 diabetes mellitus by administering polyphenon 60 (P60) and sodium ascorbate (SA) to diabetic rats induced by high fat diet/low-dose streptozotocin. The experimental group was divided into five different groups: non-diabetic control group (NC), diabetes control group (DC), diabetes+P60 group (DM+P60), diabetes+SA group (DM+SA), and diabetes+P60+SA group (DM+P60+SA). P60 and SA were dissolved in 0.1% drinking water. After P60 and SA were administered for 16 weeks, fasting blood glucose, plasma insulin, serum triglyceride, blood urea nitrogen (BUN), and creatinine levels as well as kidney alkaline phosphatase (AP) and ${\gamma}$-glutamyltranspeptidase (GGT) activities were measured. Fasting blood glucose level increased 5-fold in the DC group compared to the NC group. In the DM+P60 group, fasting blood glucose level decreased by 14%. In the DM+P60+SA group, fasting blood glucose level decreased by 28% compared to the DC group, whereas the DM+SA group did not show any significant difference. The homeostasis model assessment for insulin resistance index increased in the DC group and decreased in the DM+P60+SA group compared to the DC group. Serum creatinine level increased in the DC group, but decreased by 17% in the DM+P60 group and by 43% in the DM+P60+SA group compared to the DC group. The serum BUN level increased in the DC group, but decreased by 41% in the DM+P60+SA group compared to the DC group. Kidney GGT and AP activities decreased in the DC group compared to the NC group; however, they were reversed by DM+P60+SA group. These results show that combined administration of both green tea polyphenol and vitamin C had better effects on improving blood glucose level, insulin resistance, serum triglyceride level, and protecting kidneys than administration of either green tea polyphenol or vitamin C alone in the context of type 2 diabetes.

• Journal of Acupuncture Research
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• v.21 no.4
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• pp.177-196
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• 2004

Objective : Rheumatoid arthritis is an autoimmune disease that pathogenesis is not fully understood and one of the most intractable musculoskeletal diseases. The concern in the immunopathogenesis of rheumatoid arthritis has been increased since 1980's and many immunotherapeutic agents including disease-modifying antirheumatic drugs (DMARDs) were developed and became the mainstay of treatment of rheumatoid arthritis. However, the cure of the disease has hardly been achieved. In oriental medicine, rheumatoid arthritis is related to Bi-Zheng(痺證), that presents pain, swelling, andlor loss of joint function as major clinical manifestations, and also known to be deeply involved in suppression of immune function related to weakness of Jung-Ki(正氣). The herbal medicine, empirically used, could be a potential resource of development of new immunotherapeutic agents for rheumatoid arthritis. Methods : We developed a search strategy using terms to include "rheumatoid arthritis and herbal medicine" combined with "Chinese medicine" and/or "Oriental medicine". The search was focused on experimental studies of herbal medicine (January 1999 to May 2004), which is known to have effects on immune function of patients with rheumatoid arthritis. Computerized search used Internet databases including KISS and RISS4U (Korea), CNKI (China), MOMJ (Main Oriental Medicine Journal, Japan), and PubMed. The articles were selected from journals of universities or major research institutes. Results : The literature search for experimental studies on effects of herbal medicine on immunity of rheumatoid arthritis retrieved a total of 21 articles (Korea; 8, China ; 12, Japan ; 1). Of 21 articles, 10 were related to single-drug formula, 2 to drug interaction, and 9 to multi-drug formula. Single-drug formula was mainly used for aqua-acupuncture and researches on active components. Studies of drug interaction emphasized harmony of Ki-Hyul(氣血) and balance of Han-Yeul(寒熱). Multi-drug regimen was mainly found among formulas for Bo-Ki-Hyul(補氣血) and Bo-Sin(補腎). Conclusion : Studies on rheumatoid arthritis were performed both in vitro and in vivo in vitro study, LPS-stimulated splenocytes and synoviocytes were treated with herbal medicine, resulting in proliferation and activation of immune cells and suppression of cytokine activities in vivo study CIA animal model demonstrated that herbal medicine decreased antibody production and improved function of immune cells. In cellular and molecular study herbal medicine showed profound effects on the level of mRNA expression of certain cytokines related to immune function. This study revealed that herbal medicine has significant immune modulatory action and could be used for recovery of immune dysfunction of rheumatoid arthritis patients.

• Progress in Medical Physics
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• v.14 no.1
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• pp.34-42
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• 2003

In medical imaging, three-dimensional (3D) display using Virtual Reality Modeling Language (VRML) as a portable file format can give intuitive information more efficiently on the World Wide Web (WWW). The web-based 3D visualization of functional images combined with anatomical images has not studied much in systematic ways. The goal of this study was to achieve a simultaneous observation of 3D anatomic and functional models with planar images on the WWW, providing their locational information in 3D space with a measuring implement using VRML. MRI and ictal-interictal SPECT images were obtained from one epileptic patient. Subtraction ictal SPECT co-registered to MRI (SISCOM) was performed to improve identification of a seizure focus. SISCOM image volumes were held by thresholds above one standard deviation (1-SD) and two standard deviations (2-SD). SISCOM foci and boundaries of gray matter, white matter, and cerebrospinal fluid (CSF) in the MRI volume were segmented and rendered to VRML polygonal surfaces by marching cube algorithm. Line profiles of x and y-axis that represent real lengths on an image were acquired and their maximum lengths were the same as 211.67 mm. The real size vs. the rendered VRML surface size was approximately the ratio of 1 to 605.9. A VRML measuring tool was made and merged with previous VRML surfaces. User interface tools were embedded with Java Script routines to display MRI planar images as cross sections of 3D surface models and to set transparencies of 3D surface models. When transparencies of 3D surface models were properly controlled, a fused display of the brain geometry with 3D distributions of focal activated regions provided intuitively spatial correlations among three 3D surface models. The epileptic seizure focus was in the right temporal lobe of the brain. The real position of the seizure focus could be verified by the VRML measuring tool and the anatomy corresponding to the seizure focus could be confirmed by MRI planar images crossing 3D surface models. The VRML application developed in this study may have several advantages. Firstly, 3D fused display and control of anatomic and functional image were achieved on the m. Secondly, the vector analysis of a 3D surface model was defined by the VRML measuring tool based on the real size. Finally, the anatomy corresponding to the seizure focus was intuitively detected by correlations with MRI images. Our web based visualization of 3-D fusion image and its localization will be a help to online research and education in diagnostic radiology, therapeutic radiology, and surgery applications.

• Journal of Technology Innovation
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• v.22 no.1
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• pp.149-175
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• 2014

This study analyzed the effect of corporate innovation strategies on patent production and ultimately on technological change and new product development of firms in South Korea. The intent was to derive efficient strategies for enhancing technological performance of the firms. For the empirical analysis, three sources of data were combined: four waves of the Human Capital Corporate Panel Survey (HCCP) data collected by the Korea Research Institute for Vocational Education and Training (KRIVET), corporate financial data obtained from the Korea Information Service (KIS), and corporate patent data provided by the Korean Intellectual Property Office (KIPO). The patent production function was estimated by zero-inflated negative binomial (ZINB) regression. The technological performance function was estimated by two-stage regression, taking into account the endogeneity of patent production. An ordered logit model was applied for the second stage regression. Empirical results confirmed the critical role of corporate innovation strategies in patent production and in facilitating technological change and new product development of the firms. In patent production, the firms' R&D investment and human resources were key determinants. Higher R&D intensity led to more patents, yet with decreasing marginal productivity. A larger stock of registered patents also led to a larger flow of new patent production. Firms were more prolific in patent production when they had high-quality personnel, intensely investing in human resource development, and adopting market-leading or fast-follower strategy as compared to stability strategy. In technological performance, the firms' human resources played a key role in accelerating technological change and new product development. R&D intensity expedited new product development of the firm. Firms adopting market-leading or fast-follower strategy were at an advantage than those with stability strategy in technological performance. Firms prolific in patent production were also advanced in terms of technological change and new product development. However, the nexus between patent production and technological performance measures was substantially reduced when controlling for the endogeneity of patent production. These results suggest that firms need to strengthen the linkage between patent production and technological performance, and take strategies that address each firm's capacities and needs.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1031-1031
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• 2001

The mammary gland is made up of remarkably sensitive tissue, which has the capability of producing a large volume of secretion, milk, under normal or healthy conditions. When bacteria enter the gland and establish an infection (mastitis), inflammation is initiated accompanied by an influx of white cells from the blood stream, by altered secretory function, and changes in the volume and composition of secretion. Cell numbers in milk are closely associated with inflammation and udder health. These somatic cell counts (SCC) are accepted as the international standard measurement of milk quality in dairy and for mastitis diagnosis. NIR Spectra of unhomogenized composite milk samples from 14 cows (healthy and mastitic), 7days after parturition and during the next 30 days of lactation were measured. Different multivariate analysis techniques were used to diagnose the disease at very early stage and determine how the spectral properties of milk vary with its composition and animal health. PLS model for prediction of somatic cell count (SCC) based on NIR milk spectra was made. The best accuracy of determination for the 1100-2500nm range was found using smoothed absorbance data and 10 PLS factors. The standard error of prediction for independent validation set of samples was 0.382, correlation coefficient 0.854 and the variation coefficient 7.63%. It has been found that SCC determination by NIR milk spectra was indirect and based on the related changes in milk composition. From the spectral changes, we learned that when mastitis occurred, the most significant factors that simultaneously influenced milk spectra were alteration of milk proteins and changes in ionic concentration of milk. It was consistent with the results we obtained further when applied 2DCOS. Two-dimensional correlation analysis of NIR milk spectra was done to assess the changes in milk composition, which occur when somatic cell count (SCC) levels vary. The synchronous correlation map revealed that when SCC increases, protein levels increase while water and lactose levels decrease. Results from the analysis of the asynchronous plot indicated that changes in water and fat absorptions occur before other milk components. In addition, the technique was used to assess the changes in milk during a period when SCC levels do not vary appreciably. Results indicated that milk components are in equilibrium and no appreciable change in a given component was seen with respect to another. This was found in both healthy and mastitic animals. However, milk components were found to vary with SCC content regardless of the range considered. This important finding demonstrates that 2-D correlation analysis may be used to track even subtle changes in milk composition in individual cows. To find out the right threshold for SCC when used for mastitis diagnosis at cow level, classification of milk samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two levels of SCC - 200 000 cells/$m\ell$ and 300 000 cells/$m\ell$, respectively, were set up and compared as thresholds to discriminate between healthy and mastitic cows. The best detection accuracy was found with 200 000 cells/$m\ell$ as threshold for mastitis and smoothed absorbance data: - 98% of the milk samples in the calibration set and 87% of the samples in the independent test set were correctly classified. When the spectral information was studied it was found that the successful mastitis diagnosis was based on reviling the spectral changes related to the corresponding changes in milk composition. NIRS combined with different ways of spectral data ruining can provide faster and nondestructive alternative to current methods for mastitis diagnosis and a new inside into disease understanding at molecular level.

• The Korean Journal of Pharmacology
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• v.31 no.3
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• pp.365-375
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• 1995

The anemia-inducing strain of Friend virus (FVA) is a murine retrovirus which stimulates the proliferation of erythroid progenitor cells. The progenitor cells synthesized by FVA-stimulation are unable to proceed with differentiation and accumulate in the spleen resulting in splenomegaly in infected mice. Using FVA-inoculated mice as a model, we have investigated the antiretroviral effects of 2',3'-dideoxycytidine (ddC) and recombinant $interferon-{\alpha}-A\;(rIFN-{\alpha}-A)$ on FVA infection. The extent of the infection was determined by measuring the weights of the spleens. Daily intraperitoneal injection of ddC (100 mg/kg body weight), $rIFN-{\alpha}-A$ (10 KU/mose) and the combination of both drugs to FVA inoculated mice for 18 days resulted in suppression of the growth of spleens by 15.1%, 52.7% and 61.6%, respectively. When ddC was dissolved in drinking water (0.1 mg/ml) and administered to a group of FVA inoculated mice ad libitum, and $rIFN-{\alpha}-A$ (10 KU/mouse) was intraperitoneally injected daily to another group of ddC (0.1 mg/ml) drinking mice for 18days, the growth of spleens was suppressed by 38.4% and 83.2%, respectively. These results indicate that administration of ddC via drinking water is more effective in suppressing FVA infection than the daily injection of ddC, and that the combined effects ddC and $rIFN-{\alpha}-A$ are not synergistic but additive. In order to determine whether ddC treatment alters the characteristic of the progenitor cells with respect to $Ca^{++}$ uptake, $Ca^{++}$ uptake in erythroid cells and the effect of cyclohexyladenosine (CHA) on the $Ca^{++}$ uptake were studied. $Ca^{++}$ uptake in the erythroid progenitor cells was about 20-fold greater than in mouse erythrocytes and the inhibition of $Ca^{++}$ uptake by CHA was the greatest in the progenitor cells from FVA infected mice which were treated with ddC. The inhibition was obviated by theophylline. Results of CHA binding studies showed that the erythroid progenitor cells contain both high and low affinity CHA binding sites, whereas mose erythrocytes contain only the low affinity CHA binding sites.

• Journal of Microbiology and Biotechnology
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• v.25 no.10
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• pp.1751-1760
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• 2015

Mucin2 (MUC2), an important regulatory factor in the immune system, plays an important role in the host defense system against bacterial translocation. Probiotics known to regulate MUC2 gene expression have been widely studied, but the interactions among probiotic, pathogens, and mucin gene are still not fully understood. The aim of this study was to investigate the role of MUC2 in blocking effects of probiotics on meningitic E. coli-induced pathogenicities. In this study, live combined probiotic tablets containing living Bifidobacterium, Lactobacillus bulgaricus, and Streptococcus thermophilus were used. MUC2 expression was knocked down in Caco-2 cells by RNA interference. 5-Aza-2'-deoxycytidine (5-Aza-CdR), which enhances mucin-promoted probiotic effects through inducing production of Sadenosyl-L-methionine (SAMe), was used to up-regulate MUC2 expression in Caco-2 cells. The adhesion to and invasion of meningitic E. coli were detected by competition assays. Our studies showed that probiotic agents could block E. coli-caused intestinal colonization, bacteremia, and meningitis in a neonatal sepsis and meningitis rat model. MUC2 gene expression in the neonatal rats given probiotic agents was obviously higher than that of the infected and uninfected control groups without probiotic treatment. The prohibitive effects of probiotic agents on MUC2-knockdown Caco-2 cells infected with E44 were significantly reduced compared with nontransfected Caco-2 cells. Moreover, the results also showed that 5-Aza-CdR, a drug enhancing the production of SAMe that is a protective agent of probiotics, was able to significantly suppress adhesion and invasion of E44 to Caco-2 cells by upregulation of MUC2 expression. Taken together, our data suggest that probiotic agents can efficiently block meningitic E. coli-induced pathogenicities in a manner dependent on MUC2.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.11
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• pp.1007-1018
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• 2009

Hydrocyclone is widely used in industry, because of its simplicity in design, high capacity, low maintenance and operational cost. The separation action of a hydrocyclone treating particulate slurry is a consequence of the swirling flow that produces a centrifugal force on the fluid and suspended particles. In spite of hydrocyclone have many advantage, the application for treatment of urban stormwater case study were rare. We conducted a laboratory scale study on treatable potential of micro particles using hydrocyclone filter (HCF) that was a combined modified hydrocyclone with perlite filter cartridge. Since it was not easy to use actual storm water in the scaled-down hydraulic model investigations, it was necessary to reproduce ranges of particles sizes with synthetic materials. The synthesized storm runoff was made with water and addition of particles; ion exchange resin, road sediment, commercial area manhole sediment, and silica gel particles. Experimental studies have been carried out about the particle separation performance of HCF-open system and HCF-closed system. The principal structural differences of these HCFs are underflow zone structure and vortex finder. HCF was made of acryl resin with 120 mm of diameter hydrocyclone and 250 mm of diameter filter chamber and overall height of 800 mm. To determine the removal efficiency for various influent concentrations of suspended solids (SS) and chemical oxygen demand (COD), tests were performed with different operational conditions. The operated maximum of surface loading rate was about 700 $m^3/m^2$/day for HCF-open system, and 1,200 $m^3/m^2$/day for HCF-closed system. It was found that particle removal efficiency for the HCF-closed system is better than the HCF-open system under same surface loading rate. Results showed that SS removal efficiency with the HCF-closed system improved by about 8~20% compared with HCF-open system. The average removal efficiency difference for HCF-closed system between measurement and CFD particle tracking simulation was about 4%.