• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Journal of Acupuncture Research
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• v.37 no.4
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• pp.209-223
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• 2020

The purpose of this study was to evaluate the effectiveness of acupuncture treatment for the symptoms of Alzheimer's disease (AD). There were 11 databases searched for randomized controlled trials using acupuncture treatment for AD. The risk-of-bias assessment tool of the Cochrane Library was used to evaluate the quality of each study. Using the Review Manager (RevMan), a meta-analysis was performed using risk ratio, mean difference, 95% confidence interval, and random effect model. There were 32 studies selected, all of which were conducted in China. There were 16 studies where acupuncture was used as the intervention, and 23 studies used Donepezil as the control group. Acupoint GV20 was most frequently selected during the treatment. The Mini Mental State Examination (MMSE) was the most frequently used outcome variable. A period of 12 weeks was the most common treatment duration. As a result of meta-analysis (n = 25), acupuncture improved the MMSE and activities of daily living scores. Electroacupuncture improved the Alzheimer Disease Assessment Scale-Cognitive Subscale, and scalp acupuncture improved the MMSE score for AD. Acupuncture alleviates the symptoms of AD. However, further research is necessary to provide a better level of evidence.

• Tuberculosis and Respiratory Diseases
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• v.73 no.3
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• pp.162-168
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• 2012

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive disease. Effective treatment is not currently available and the prognosis is poor. The aim of our study was to identify clinical predictors of survival in patients with IPF. Methods: By using medical record database of a university hospital, we reviewed the records of patients who had been diagnosed as having IPF from January 1996 through December 2007. Results: Among 89 patients considered as having interstitial lung disease (ILD) on computed tomography (CT) of the chest, 22 were excluded because of the diagnosis of other ILDs or connective tissue disease, and finally, 67 met the criteria of IPF. The mean age at the diagnosis of IPF was 70 years (range, 41~87 years) and 43 (64%) were male. The mean survival time following the diagnosis of IPF was 40 months (range, 0~179 months). Among them, 28 cases were diagnosed as the progressive state of IPF on the follow-up CT examination, and the mean duration between diagnosis of IPF and progression was 31 months. Multivariate analysis using Cox regression model revealed that body mass index (BMI) less than 18.5 $kg/m^2$ (p=0.030; hazard ratio [HR], 12.085; 95% confidence interval [CI], 1.277~114.331) and CT progression before 36 months from the diagnosis of IPF (p=0.042; HR, 13.564; 95% CI, 1.101~167.166) were independently associated with mortality. Conclusion: Since low BMI at the diagnosis of IPF and progression on follow-up CT were associated with poor prognosis, IPF patients with low BMI and/or progression before 36 months following the diagnosis should be closely monitored.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.8
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• pp.730-740
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• 2000

The software rejuvenation method for highly available multiplex systems uses a pro-active fault-tolerant approach to handle system failures. The software rejuvenation prevents failures from occurring, while the previous methods recover from failures after happening. Especially, since the software aging proceeds fast in the software used for the multimedia mobile computing due to the loss of communications or data, the preventive method from failures using software rejuvenation can be used for the multimedia mobile computing. In this paper, according to the operational parameters such as rejuvenation period, rejuvenation time, failure rate and repair rate of the servers, number of running servers, duration of running time, and type of running modes, we calculate steady-state probabilities, downtime, availability, and cost of the multiplex systems using software rejuvenation method. We validate the closed-form solutions of the mathematical model by experiments based on various operational parameters and find that the software rejuvenation method can be adopted as preventive fault-tolerant technique. The failure rate and unstable rate of the servers are essential factors for the decision making of the rejuvenation policies.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.5
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• pp.49-62
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• 2021

The objective of this study was to evaluate the flood control capacity of the agricultural reservoir based on state-of-the-art climate change scenario - SSP (Shared Socioeconomic Pathways). 18 agricultural reservoirs were selected as the study sites, and future rainfall data based on SSP scenario provided by CMIP6 (Coupled Model Intercomparison Project 6) was applied to analyze the impact of climate change. The frequency analysis module, the rainfall-runoff module, the reservoir operation module, and their linkage system were built and applied to simulate probable rainfall, maximum inflow, maximum outflow, and maximum water level of the reservoirs. And the maximum values were compared with the design values, such as design flood of reservoirs, design flood of direct downstream, and top of dam elevation, respectively. According to whether or not the maximum values exceed each design value, cases were divided into eight categories; I-O-H, I-O, I-H, I, O-H, O, H, X. Probable rainfall (200-yr frequency, 12-h duration) for observed data (1973~2020) was a maximum of 445.2 mm and increased to 619.1~1,359.7 mm in the future (2011~2100). For the present, 61.1% of the reservoirs corresponded to I-O, which means the reservoirs have sufficient capacity to discharge large inflow; however, there is a risk of overflowing downstream due to excessive outflow. For the future, six reservoirs (Idong, Baekgok, Yedang, Tapjung, Naju, Jangsung) were changed from I-O to I-O-H, which means inflow increases beyond the discharge capacity due to climate change, and there is a risk of collapse due to dam overflow.

• Journal of Animal Science and Technology
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• v.65 no.5
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• pp.939-950
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• 2023

This study was conducted to assess the impact of growing condition variables on alfalfa (Medicago sativa L.) productivity. A total of 197 alfalfa yield results were acquired from the alfalfa field trials conducted by the South Korean National Agricultural Cooperative Federation or Rural Development Administration between 1983 and 2008. The corresponding climate and soil data were collected from the database of the Korean Meteorological Administration. Twenty-three growing condition variables were developed as explaining variables for alfalfa forage biomass production. Among them, twelve variables were chosen based on the significance of the partial-correlation coefficients or potential agricultural values. The selected partial correlation coefficients between the variables and alfalfa forage biomass ranged from -0.021 to 0.696. The influence of the selected twelve variables on yearly alfalfa production was summarized into three dominant factors through factor analysis. Along with the accumulated temperature variables, the loading scores of the daily mean temperature higher than 25℃ were over 0.88 in factor 1. The sunshine duration at temperature between 0℃-25℃ was 0.939 in factor 2. Precipitation days were 0.82, which was the greatest in factor 3. Stepwise regression applied with the three dominant factors resulted in the coefficients of factors 1, 2, and 3 for 0.633, 0.485, and 0.115, respectively, and the R-square of the model was 0.602. The environmental conditions limiting alfalfa growth, such as daily temperature higher than 25℃ or daily mean temperature affected annual alfalfa production most substantially among the growing condition variables. Therefore, future cultivar selection should consider the capability of alfalfa to be tolerant to extreme summer weather along with biomass production potential.

• The Korean Journal of Urological Oncology
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• 제15권3호
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• pp.178-186
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• 2017

Purpose: Poloxamer 407 (P407) thermo-sensitive hydrogel formulations were developed to enhance the retention time in the urinary bladder after intravesical instillation. Materials and Methods: P407 hydrogels (P407Gels) containing 0.2 w/w% fluorescein isothiocyanate dextran (FD, MW 4 kDa) as a fluorescent probe were prepared by the cold method with different concentrations of the polymer (20, 25, and 30 w/w%). The gel-forming capacities were characterized in terms of gelation temperature (G-Temp), gelation time (G-Time), and gel duration (G-Dur). Homogenous dispersion of the probe throughout the hydrogel was observed by using fluorescence microscopy. The in vitro bladder simulation model was established to evaluate the retention and drug release properties. P407Gels in the solution state were administered to nude mice via urinary instillation, and the in vivo retention behavior of P407Gels was visualized by using an in vivo imaging system (IVIS). Results: P407Gels showed a thermo-reversible phase transition at $4^{\circ}C$ (refrigerated; sol) and $37^{\circ}C$ (body temperature; gel). The G-Temp, G-Time, and G-Dur of FD-free P407Gels were approximately $10^{\circ}C-20^{\circ}C$, 12-30 seconds, and 12-35 hours, respectively, and were not altered by the addition of FD. Fluorescence imaging showed that FD was spread homogenously in the gelled P407 solution. In a bladder simulation model, even after repeated periodic filling-emptying cycles, the hydrogel formulation displayed excellent retention with continuous release of the probe over 8 hours. The FD release from P407Gels and the erosion of the gel, both of which followed zero-order kinetics, had a linear relationship ($r^2=0.988$). IVIS demonstrated that the intravesical retention time of P407Gels was over 4 hours, which was longer than that of the FD solution (<1 hour), even though periodic urination occurred in the mice. Conclusions: FD release from P407Gels was erosion-controlled. P407Gels represent a promising system to enhance intravesical retention with extended drug delivery.

• The Korean Journal of Physiology and Pharmacology
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• v.20 no.1
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• pp.83-89
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• 2016

Sepsis is the life-threatening response to infection which can lead to tissue damage, organ failure, and death. In the current study, the effect of orally administered D-glucose on the mortality and the blood glucose level induced by D-Galactosamine (GaLN)/lipopolysaccharide (LPS)-induced sepsis was examined in ICR mice. After various amounts of D-glucose (from 1 to 8 g/kg) were orally fed, sepsis was induced by injecting intraperitoneally (i.p.) the mixture of GaLN /LPS. Oral pre-treatment with D-glucose dose-dependently increased the blood glucose level and caused a reduction of sepsis-induced mortality. The oral post-treatment with D-glucose (8 g/kg) up to 3 h caused an elevation of the blood glucose level and protected the mortality observed in sepsis model. However, D-glucose post-treated at 6, 9, or 12 h after sepsis induction did not affect the mortality and the blood glucose level induced by sepsis. Furthermore, the intrathecal (i.t.) pretreatment once with pertussis toxin (PTX; $0.1{\mu}g/5ml$) for 6 days caused a reduction of D-glucose-induced protection of mortality and hyperglycemia. Furthermore, once the hypoglycemic state is continued up to 6 h after sepsis initiated, sepsis-induced mortality could not be reversed by D-glucose fed orally. Based on these findings, it is assumed that the hypoglycemic duration between 3 and 6 h after the sepsis induction may be a critical time of period for the survival. D-glucose-induced protective effect against sepsis-induced mortality appears to be mediated via activating PTX-sensitive G-proteins in the spinal cord. Finally, the production of hyperglycemic state may be critical for the survival against the sepsis-induced mortality.

• Journal of Preventive Medicine and Public Health
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• v.38 no.2
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• pp.208-214
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• 2005

Objectives: This study was performed to examine the factors affecting re-smoking in male workers. Methods: A self-administrated questionnaire survey was conducted during April 2003 to examine the smoking state of 1,154 employees of a company that launched a smoking cessation campaign in1998. Five hundred and eighty seven persons, who had stopped smoking for at least one week, were selected as the final study subjects. This study collected data on smoking cessation success or failure for 6 months, and looked at the factors having an effect on re-smoking within this period. This study employed the Health Belief Model as its theoretical basis. Results: The re-smoking rate of the 587 study subjects who had stopped smoking for at least one week was 44.8% within the 6 month period. In a simple analysis, the re-smoking rates were higher in workers with a low age, on day and night shifts, blue collar, of a low rank, where this was their second attempt at smoking cessation and for those with a shorter job duration (p<0.05). Of the cues to action variables in the Heath Belief Model, re-smoking was significantly related with the perceived susceptibility factor, economic advantages of smoking cessation among the perceived benefits factor, the degree of cessation trial's barrier of the perceived barriers factor, smoking symptom experience, recognition of the degree of harmfulness of environmental tobacco smoke and the existence of chronic disease due to smoking (p<0.05). In the multiple logistic regression analysis for re-smoking, the significant variables were age, perceived susceptibility for disease, economic advantages due to smoking cessation, the perceived barrier for smoking cessation, recognition on the degree of harmfulness of environmental tobacco smoke, the existence of chronic disease due to smoking and the number of attempts at smoking cessation (p<0.05). Conclusion: From the result of this study, for an effective smoking ban policy within the work place, health education that improves the knowledge of the adverse health effects of smoking and the harmfulness of environmental tobacco smoke will be required, as well as counter plans to reduce the barriers for smoking cessation.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3917-3925
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• 2012

Background: The relationship between postmenopausal hormone therapy (HT) and invasive breast cancer has been extensively investigated, but that with breast carcinoma in situ (BCIS) has received relatively little attention. The aim of our present study was to review and summarize the evidence provided by longitudinal studies on the association between postmenopausal HT use and BCIS risk. Methods: A comprehensive literature search for articles published up to May 2012 was performed. Prior to performing a meta-analysis, the studies were evaluated for publication bias and heterogeneity. Relative risk (RR) or odds ratio (OR) values were calculated using 14 reports (8 case-control studies and 6 cohort studies), published between 1986 and 2012. Results: There was evidence of an association between ever postmenopausal estrogen use and BCIS based on a random-effects model (RR = 1.25, 95% confidence interval (CI) = 1.01, 1.55). However, we found no strong evidence of an association between ever postmenopausal estrogen combined with progesterone use and BCIS using a randomeffects model (RR = 1.55, 95% CI = 0.95, 2.51). Furthermore, our analysis showed a strong association between "> 5 years duration" of estrogen or estrogen combined with progesterone use and BCIS. Furthermore, current use of any HT is associated with increased risk of BCIS in cohort studies. Additional well-designed large studies are now required to validate this association in different populations.