• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.45-67
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• 2017

According to the Internet Usage Research performed in 2016, the number of internet users and the internet usage have been increasing. Smartphone, compared to the computer, is taking a more dominant role as an internet access device. As the number of smart devices have been increasing, some views that the demand on high-speed internet will decrease; however, Despite the increase in smart devices, the high-speed Internet market is expected to slightly increase for a while due to the speedup of Giga Internet and the growth of the IoT market. As the broadband Internet market saturates, telecom operators are over-competing to win new customers, but if they know the cause of customer exit, it is expected to reduce marketing costs by more effective marketing. In this study, we analyzed the relationship between the cancellation rates of telecommunication products and the factors affecting them by combining the data of 3 cities, Anyang, Gunpo, and Uiwang owned by a telecommunication company with the regional data from KOSIS(Korean Statistical Information Service). Especially, we focused on the assumption that the neighboring areas affect the distribution of the cancellation rates by coupling type, so we conducted spatial cluster analysis on the 3 types of cancellation rates of each region using the spatial analysis tool, SatScan, and analyzed the various relationships between the cancellation rates and the regional data. In the analysis phase, we first summarized the characteristics of the clusters derived by combining spatial information and the cancellation data. Next, based on the results of the cluster analysis, Variance analysis, Correlation analysis, and regression analysis were used to analyze the relationship between the cancellation rates data and regional data. Based on the results of analysis, we proposed appropriate marketing methods according to the region. Unlike previous studies on regional characteristics analysis, In this study has academic differentiation in that it performs clustering based on spatial information so that the regions with similar cancellation types on adjacent regions. In addition, there have been few studies considering the regional characteristics in the previous study on the determinants of subscription to high-speed Internet services, In this study, we tried to analyze the relationship between the clusters and the regional characteristics data, assuming that there are different factors depending on the region. In this study, we tried to get more efficient marketing method considering the characteristics of each region in the new subscription and customer management in high-speed internet. As a result of analysis of variance, it was confirmed that there were significant differences in regional characteristics among the clusters, Correlation analysis shows that there is a stronger correlation the clusters than all region. and Regression analysis was used to analyze the relationship between the cancellation rate and the regional characteristics. As a result, we found that there is a difference in the cancellation rate depending on the regional characteristics, and it is possible to target differentiated marketing each region. As the biggest limitation of this study and it was difficult to obtain enough data to carry out the analyze. In particular, it is difficult to find the variables that represent the regional characteristics in the Dong unit. In other words, most of the data was disclosed to the city rather than the Dong unit, so it was limited to analyze it in detail. The data such as income, card usage information and telecommunications company policies or characteristics that could affect its cause are not available at that time. The most urgent part for a more sophisticated analysis is to obtain the Dong unit data for the regional characteristics. Direction of the next studies be target marketing based on the results. It is also meaningful to analyze the effect of marketing by comparing and analyzing the difference of results before and after target marketing. It is also effective to use clusters based on new subscription data as well as cancellation data.

• Journal of East-West Nursing Research
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• v.7 no.1
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• pp.68-81
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• 2002

The purpose of this study is to analyze the type of ego states and stress coping style on female college students who are in the course of nursing study. This study is performed in the view of Transactional Analysis and designed to scrutinize descriptive correlations between the type of ego states and stress coping style. The subject is consists of 144 freshmen and sophomore, 138 junior and senior students group, who are students of K nursing college located in Seoul. The sampling investigation period is on Sept. 14, 2002 to Oct. 26, 2002. The measuring instrument used for Transactional Analysis ego state is 50 items Ego-gram research paper devised by Dusay(1997). For studying coping style, Folkman & Lazarus's measurement(1984) was adopted, which is translated and modified by Han, and Oh,(1990). Health states is adopted by standardized health inspecting instrumental table (Cornell Medical Index:CMI) which is designed for Korean people by Ko and Park(1980) Statistic average and standard deviation were generated by using SPSS PC+, t=test and Pearson correlation. The results were as follows: 1) In the type of ego states on both groups indicated the arithmetic apex NP(maximum value), then the point A was high and the data made a down slope to point AC. In the comparison to type of ego states between two groups, only at point CP, the data value of upper year students represented higher than that of lower year ones by c(t=2.28, p=.023). 2) Stress coping style of whole students were highly and affirmatively dedicated to research. Especially hopeful aspect(t=.67, p=.05), relaxation of tension(t=-2.16, p=.03) made significant difference each other in the view of arithmetic calculation. 3) In view of nursing students' physical health states, there is significant difference in past history(t=2.50, p=.013) and in case of mental health states, there are considerable discrepancies between lower group(73.52) and upper group(75.11)(p<.05). In view of all field, state of tension(t=2.13, p=.048) has difference. 4) While verifying coping style in terms of ego states level between lower and upper students group, In type CP, high level ego states group indicated significant difference on stress coping style area than low leveled group and made such sequences as the central point of problem, In type NP, sequences such as the central point of problem, In type A, the central point of problem, In type FC, hopeful aspect and In type AC, hopeful aspect and indifference were derived significantly different (p<.05). 5) While verifying health state differences in the level of lower and upper ego states, In type FC, low level group(150.29) marked higher point than upper group(145.19), there is remarkable discrepancy and so did whole health state(p=.014), In type AC both mental state(p=.000) and whole health state (p=.015) showed differences. 6) When analyzing correlations between whole students' ego states, copying style and health state, all type of ego state showed differences(p<.001). In correlations between ego state and health state, in type FC physical state had an apex and there are inverse correlations among the other types. Especially, type FC showed inverse correlations with great discrepancies(p<.05). In mental state, type NP(${\gamma}=.198$, p<.001) and type A(${\gamma}=.166$, p<.05) represented straight correlations with remarkable differences. Especially, In type AC showed inverse correlations(${\gamma}=.282$, p<.001). In case of correlations between copying style and health state, indifference(${\gamma}=-.157$) and relaxation of tension(${\gamma}=-.158$) presented great difference(p<.05). In mental state, central point of problem and search for social support showed straight correlations with great discrepancies(p<.05), hopeful aspect and indifference showed inverse correlations with considerable differences(p<.001).

• Health Policy and Management
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• v.14 no.1
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• pp.65-88
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• 2004

In general, empowerment is defined as the motivational concept of autonomy and self-efficacy. Recently, the concept of empowerment is applied to improve organizational staff's job satisfaction and organizational commitment in many organizations. Empower-ment in service organizations, i.e., hospitals, has certainly generated more publicity than any other organizations. The objectives of this study are, first, to measure the degree of hospital employees' empowerment using Spreitzer(1995)'s empowerment theory, second, to analyze the causal relationship of organizational and job characteristics, a degree of empowerment, and organizational performance(job satisfaction and organizational commitment), and third, to offer the strategy for the improvement of job satisfaction and organizational commitment. Spreitzer insists that the empowerment is composed of 4 dimensions(meaning, competence, self-determination, and impact). And he argues that various work-related characteristics is a direct cause of empowerment and the indirect cause of job satisfaction and organizational commitment, mediated by the empowerment latent variable. In order to perform this study, data were collected by self-administered questionnaires from hospital employees working in administrative department of 3 university hospitals in Inchon and Kyunggi-Do region. The number of cases is 181(response rate; 86%). The Collected data were analyzed with SPSS Ver. 10.0 and AMOSV Ver. 4.0. First, to test validity of variables, the factor analysis was used. Second, to test reliability, Cronbach's alpha coefficients was calculated. Cronbach's alpha of empowerment variable is 0.8323 showing that there's no problem in regard to the internal consistency. Also the Cronbach's alpha of other variables are 0.8301 of the degree of perceived control, 0.6705 of job characteristics, O.8787 of compensation, 0.9254 of job satisfaction, and 0.8389 of organizational commitment, respectively. Among the questions of job characteristics, two survey questions are deleted due to lowering the reliability. Third, to test multicollinearity and correlation of variables, the correlation analysis was performed. There was no problem of multicollinearity. Finally structural equation modelling (SEM) analysis was conducted to find the causal relationship of organizational and job Characteristics, empowerment, job satisfaction and organizational commitment. The 16 variables are included for the SEM analysis. The major results of this study are as follows: First, in the case of model fitness, the condition of x$^2$ statistic(92.187) is not fully satisfied, but the indices of GFI(0.912), AGFI(0.863), NFI(0.917) and CFI(0.928) are partially satisfied, which needs to upper 0.90. Second, in the result of hypotheses testing, all hypotheses are accepted and have a positive effect in 95% or 99% confidence interval(P<0.05 or P<0.001) except the effect of compensation variable on empowerment(P=0.082). Third, in regard to the direct, indirect, and total effect of variables, the direct effect of perceived control, task characteristics, and compensation on job satisfaction are 0.728, 2.264, 0.328 and on organizational commitment are 0.094, 1.411, 0.418, respectively. Also the indirect effect of perceived control, task characteristics, and compensation on job satisfaction are 0.311, 0.196, 0.028 and on organizational commitment are 0.210, 0.132, 0.019, respectively. Thus, these findings imply that various work-related factors are direct effect of empowerment and indirect effect of result variables, job satisfaction and organizational commitment. Also These results showed that the workplace empowerment is significant mediating factor of employee's job satisfaction and organizational commitment.

• Journal of Magnetics
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• v.20 no.4
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• pp.381-386
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• 2015

The purpose of this study is to improve diagnostic efficiency of clinical study by setting up guidelines for more precise examination with a comparative analysis of signal intensity and image distortion depending on the location of X axial of object when performing magnetic resonance diffusion weighted imaging (MR DWI) examination. We arranged the self-produced phantom with a 45 mm of interval from the core of 44 regent bottles that have a 16 mm of external diameter and 55 mm of height, and were placed in 4 rows and 11 columns in an acrylic box. We also filled up water and margarine to portrait the fat. We used 3T Skyra and 18 Channel Body array coil. We also obtained the coronal image with the direction of RL (right to left) by using scan slice thinkness 3 mm, slice gap: 0mm, field of view (FOV): $450{\times}450mm^2$, repetition time (TR): 5000 ms, echo time (TE): 73/118 ms, Matrix: $126{\times}126$, slice number: 15, scan time: 9 min 45sec, number of excitations (NEX): 3, phase encoding as a diffusion-weighted imaging parameter. In order to scan, we set b-value to $0s/mm^2$, $400s/mm^2$, and $1,400s/mm^2$, and obtained T2 fat saturation image. Then we did a comparative analysis on the differences between image distortion and signal intensity depending on the location of X axial based on iso-center of patient's table. We used "Image J" as a comparative analysis programme, and used SPSS v18.0 as a statistic programme. There was not much difference between image distortion and signal intensity on fat and water from T2 fat saturation image. But, the average value depends on the location of X axial was statistically significant (p < 0.05). From DWI image, when b-value was 0 and 400, there was no significant difference up to $2^{nd}$ columns right to left from the core of patient's table, however, there was a decline in signal intensity and image distortion from the $3^{rd}$ columns and they started to decrease rapidly at the $4^{th}$ columns. When b-value was 1,400, there was not much difference between the $1^{st}$ row right to left from the core of patient's table, however, image distortion started to appear from the $2^{nd}$ columns with no change in signal intensity, the signal was getting decreased from the $3^{rd}$ columns, and both signal intensity and image distortion started to get decreased rapidly. At this moment, the reagent bottles from outside out of 11 reagent bottles were not verified from the image, and only 9 reagent bottles were verified. However, it was not possible to verify anything from the $5^{th}$ columns. But, the average value depends on the location of X axial was statistically significant. On T2 FS image, there was a significant decline in image distortion and signal intensity over 180mm from the core of patient's table. On diffusion-weighted image, there was a significant decline in image distortion and signal intensity over 90 mm, and they became unverifiable over 180 mm. Therefore, we should make an image that has a diagnostic value from examinations that are hard to locate patient's position.

• Science of Emotion and Sensibility
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• v.6 no.4
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• pp.9-14
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• 2003

Sleep stages have been useful indicator to check a person's comfortableness in a sleep, But the traditional method of scoring sleep stages with polysomnography based on the integrated analysis of the electroencephalogram(EEG), electrooculogram(EOG), electrocardiogram(ECG), and electromyogram(EMG) is too restrictive to take a comfortable sleep for the participants, While the sympathetic nervous system is predominant during a wakefulness, the parasympathetic nervous system is more active during a sleep, Cardiovascular function is controlled by this autonomic nervous system, So, we have interpreted the heart rate variability(HRV) among sleep stages to find a simple method of classifying sleep stages, Six healthy male college students participated, and 12 night sleeps were recorded in this research, Sleep stages based on the "Standard scoring system for sleep stage" were automatically classified with polysomnograph by measuring EEG, EOG, ECG, and EMG(chin and leg) for the six participants during sleeping, To extract only the ECG signals from the polysomnograph and to interpret the HRV, a Sleep Data Acquisition/Analysis System was devised in this research, The power spectrum of HRV was divided into three ranges; low frequency(LF), medium frequency(MF), and high frequency(HF), It showed that, the LF/HF ratio of the Stage W(Wakefulness) was 325% higher than that of the Stage 2(p＜.05), 628% higher than that of the Stage 3(p＜.001), and 800% higher than that of the Stage 4(p＜.001), Moreover, this ratio of the Stage 4 was 427% lower than that of the Stage REM (rapid eye movement) (p＜.05) and 418% lower than that of the Stage l(p＜.05), respectively, It was observed that the LF/HF ratio decreased monotonously as the sleep stage changes from the Stage W, Stage REM, Stage 1, Stage 2, Stage 3, to Stage 4, While the difference of the MF/(LF+HF) ratio among sleep Stages was not significant, it was higher in the Stage REM and Stage 3 than that of in the other sleep stages in view of descriptive statistic analysis for the sample group.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Korean Journal of Psychosomatic Medicine
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• v.25 no.2
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• pp.153-165
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• 2017

Objectives : Recent neuroimaging studies focus on dysfunctions in connectivity between cognitive circuits and emotional circuits: anterior cingulate cortex that connects dorsolateral orbitofrontal cortex and prefrontal cortex to limbic system. Previous studies on pediatric depression using DTI have reported decreased neural connectivity in several brain regions, including the amygdala, anterior cingulate cortex, superior longitudinal fasciculus. We compared the neural connectivity of psychotropic drug naïve adolescent patients with a first onset of major depressive episode with healthy controls using DTI. Methods : Adolescent psychotropic drug naïve patients(n=26, 10 men, 16 women; age range, 13-18 years) who visited the Korea University Guro Hospital and were diagnosed with first onset major depressive disorder were registered. Healthy controls(n=27, 5 males, 22 females; age range, 12-17 years) were recruited. Psychiatric interviews, complete psychometrics including IQ and HAM-D, MRI including diffusion weighted image acquisition were conducted prior to antidepressant administration to the patients. Fractional anisotropy(FA), radial, mean, and axial diffusivity were estimated using DTI. FMRIB Software Library-Tract Based Spatial Statistics was used for statistical analysis. Results : We did not observe any significant difference in whole brain analysis. However, ROI analysis on right superior longitudinal fasciculus resulted in 3 clusters with significant decrease of FA in patients group. Conclusions : The patients with adolescent major depressive disorder showed statistically significant FA decrease in the DTI-based structure compared with healthy control. Therefore we suppose DTI can be used as a bio-marker in psychotropic drug-naïve adolescent patients with first onset major depressive disorder.

• Korean Journal of Oriental Medicine
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• v.6 no.1
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• pp.29-46
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• 2000

The clinical application of constitutional Diagnosis is the most important part of Sasang constitutional medicine. It has been studied in various way. The study of morphologic characteristics on the head and face has been identified but didn't considered the Variations of age and sex. For the statistic analysis of the correlation between the sasang constitution and the shape of the face, the head-facial part of 182 cases(the group of throughout the age) and 69 cases(the group of age 30 to 40) were measured by Martin's measurement and analysis of a) the measurement value of height and the component ratio from the Gnathion to each part of face by constitution. b) the measurement value of depth and the component ratio from T-projected to each part of the face by constitution. c) the measurement value of breadth and component ratio between each parts of the facial breadth by constitution. d) the characteristics on each part of face by constitution. e) the result of discriminant analysis about the constitution Authors obtained the results from the study as follows: 1. Taeum-In group is characterized by the value of variables had a tendency to maximum value in throughout the age, the charateristics that the total group show is the shape of face is wide shape in horizontally and flat, the nasal breadth is wider than other constitutions, the lips is narrower than Soyang-In in horizontally and thick shape in vertically, and the biogonial breadth is more developed than other constitutions, so lower face is developed. The charateristics that only the age of 30 40 group show is that the lips is thicker than Soeum-IN. The projection of inter-eyebrows is more projected than Soyang-IN. 2. Soeum-IN group is characterized by the value of variables had a tendency to minimum value in throughout the age. The characteristics that the total group show is the lips is thiner than other constitutions, the breadth of eyes is wider than other constitutions, the difference between sellion and nasal breadth is to be little. The charateristics that only the age of 30 40 group show is that the ratio of upper face in physiognomic face is lager than Soyang-IN. 3. The total age group of Soyang-In is characterized by the shape of face is long in vertically and narrow in horizontally, the total eyebrow breadth and lips and philtrun is wider than other constitutions. The charateristics that only the age of 30 40 group is characterized by the projection of sellion is more projected than Taeum-IN. 4. The values which showed significance in both age group V76, V52, V54, V55, V57, V59, V64, V65, V67, V88, V89, V148, V150, V151, V155, V160, V161, V28, V50, V99, V102, V167, V169, V173, V175, V177, V181 are 27 in all. 5. The values which was significant in the age 30 to 40 group V77, V78, V79, V109, V140, V142, V143, V166, V174 are 9 in all.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.54-60
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• 2010

Purpose: Nuclear medicine manufacturers provide various softwares which shorten imaging time using their own image processing techniques such as UlatraSPECT, ASTONISH, Flash3D, Evolution, and nSPEED. Seoul National University Hospital has introduced softwares from Siemens and Philips, but it was still hard to understand algorithm difference between those two softwares. Thus, the purpose of this study was to figure out the difference of two softwares in planar images and research the possibility of application to images produced with high energy isotopes. Materials and Methods: First, a phantom study was performed to understand the difference of softwares in static studies. Various amounts of count were acquired and the images were analyzed quantitatively after application of PIXON, Siemens and ASTONISH, Philips, respectively. Then, we applied them to some applicable static studies and searched for merits and demerits. And also, they have been applied to images produced with high energy isotopes. Finally, A blind test was conducted by nuclear medicine doctors except phantom images. Results: There was nearly no difference between pre and post processing image with PIXON for FWHM test using capillary source whereas ASTONISH was improved. But, both of standard deviation(SD) and variance were decreased for PIXON while ASTONISH was highly increased. And in background variability comparison test using IEC phantom, PIXON has been decreased over all while ASTONISH has shown to be somewhat increased. Contrast ratio in each spheres has also been increased for both methods. For image scale, window width has been increased for 4~5 times after processing with PIXON while ASTONISH showed nearly no difference. After phantom test analysis, ASTONISH seemed to be applicable for some studies which needs quantitative analysis or high contrast, and PIXON seemed to be applicable for insufficient counts studies or long time studies. Conclusion: Quantitative values used for usual analysis were generally improved after application of the two softwares, however it seems that it's hard to maintain the consistency for all of nuclear medicine studies because result images can not be the same due to the difference of algorithm characteristic rather than the difference of gamma cameras. And also, it's hard to expect high image quality with the time shortening method such as whole body scan. But it will be possible to apply to static studies considering the algorithm characteristic or we can expect a change of image quality through application to high energy isotope images.