• The Korean Journal of Nuclear Medicine
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• v.37 no.2
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• pp.110-119
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• 2003

Purpose: Radioiodine (I-131) therapy is an effective modality to reduce both recurrence and mortality rates in differentiated thyroid cancer. Whether higher doses shows higher therapeutic responses was still debatable. The purpose of this study was to validate curve-fitting (CF) method measuring maximum permissible dose (MPD) by a biological dosimetry using metaphase analysis of peripheral blood lymphocytes. Materials and Methods: Therapeutic effects of MPD was evaluated in 58 patients (49 females and 9 males, mean age $50{\pm}11$ years) of papillary thyroid cancer. Among them 43 patients were treated with ${\Leq}7.4GBq$, while 15 patients with ${\geq}9.25GBq$. The former was defined as low-dose group, and the latter high-dose group. Therapeutic response was defined as complete response when complete disappearance of lesions on follow-up I-131 scan and undetectable serum thyroglobulin levels were found. Statistical comparison between groups were done using chi-square test. P value less than 0.05 was regarded as statistically significant. Results: MPD measured by CF method using tracer and therapeutic doses were $13.3{\pm}1.9\;and\;13.8{\pm}2.1GBq$, respectively (p=0.20). They showed a significant correlation (r=0.8, p<0.0001). Exposed doses to blood measured by CF and biological methods were $1.54{\pm}0.03\;and\;1.78{\pm}0.03Gy$ (p=0.01). They also showed a significant correlation (r=0.86, p=0.01). High-dose group showed a significantly higher rate of complete response (12/15, 80%) as compared to the low-dose group (22/43, 51.2%) (p=0.05). While occurrence of side effects was not different between two groups (40% vs. 30.2%, p=0.46). Conclusion: Measurement of MPD using CF method is reliable, and the high-dose I-131 therapy using MPD gains significantly higher therapeutic effects as compared with low-dose therapy.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.1
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• pp.9-15
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• 2007

Purpose: Repetitive transcranial magnetic stimulation (rTMS) has recently been clinically applied in the treatment of drug resistant depressed patients. There are mixed findings about the efficacy of rTMS on depression. Furthermore, the influence of rTMS on the physiology of the brain is not clear. We prospectively evaluated changes of regional cerebral blood flow (rCBF) between pre- and post-rTMS treatment in patients with drug resistant depression. Materials and Methods: Twelve patients with drug-resistant depression (7 male, 5 female; age range: $19{\sim}52$ years; mean age: $29.3{\pm}9.3$ years) were given rTMS on right prefrontal lobe with low frequency (1 Hz) and on left prefrontal lobe with high frequency (20 Hz), with 20-minute-duration each day for 3 weeks. Tc-99m ECD brain perfusion SPECT was obtained before and after rTMS treatment. The changes of cerebral perfusion were analyzed using statistical parametric mapping (SPM; t=3.14, uncorrected p<0.01, voxel=100). Results: Following areas showed significant increase in rCBF after 3 weeks rTMS treatment: the cingulate gyrus, fusiform gyrus of right temporal lobe, precuneus, and left lateral globus pallidus. Significant decrement was noted in: the precental and middle frontal gyrus of right frontal lobe, and fusiform gyrus of left occipital lobe. Conclusion: Low-frequency rTMS on the right prefrontal cortex and high-frequency rTMS on the left prefrontal cortex for 3 weeks as an add-on regimen have increased and decreased rCBF in the specific brain regions in drug-resistant depressed patients. Further analyses correlating clinical characteristics and treatment paradigm with functional imaging data may be helpful in clarifying the pathophysiology of drug-resistant depressed patients.

• 한국유가공학회:학술대회논문집
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• 2007.09a
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• pp.49-58
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• 2007

Inflammatory process leads to the well-known mucosal damage and therefore a further disturbance of the epithelial barrier function, resulting abnormal intestinal wall function, even further accelerating the inflammatory process[1]. Despite of the records, etiology and pathogenesis of IBD remain rather unclear. There are many studies over the past couple of years have led to great advanced in understanding the inflammatory bowel disease(IBD) and their underlying pathophysiologic mechanisms. From the current understanding, it is likely that chronic inflammation in IBD is due to aggressive cellular immune responses including increased serum concentrations of different cytokines. Therefore, targeted molecules can be specifically eliminated in their expression directly on the transcriptional level. Interesting therapeutic trials are expected against adhesion molecules and pro-inflammatory cytokines such as TNF-${\alpha}$. The future development of immune therapies in IBD therefore holds great promises for better treatment modalities of IBD but will also open important new insights into a further understanding of inflammation pathophysiology. Treatment of cytokine inhibitors such as Immunex(Enbrel) and J&J/Centocor(Remicade) which are mouse-derived monoclonal antibodies have been shown in several studies to modulate the symptoms of patients, however, theses TNF inhibitors also have an adverse effect immune-related problems and also are costly and must be administered by injection. Because of the eventual development of unwanted side effects, these two products are used in only a select patient population. The present study was performed to elucidate the ability of TNF-${\alpha}$ antibodies produced in sheep colostrums to neutralize TNF-${\alpha}$ action in a cell-based bioassay and in a small animal model of intestinal inflammation. In vitro study, inhibitory effect of anti-TNF-${\alpha}$ antibody from the sheep was determined by cell bioassay. The antibody from the sheep at 1 in 10,000 dilution was able to completely inhibit TNF-${\alpha}$ activity in the cell bioassay. The antibodies from the same sheep, but different milkings, exhibited some variability in inhibition of TNF-${\alpha}$ activity, but were all greater than the control sample. In vivo study, the degree of inflammation was severe to experiment, despite of the initial pilot trial, main trial 1 was unable to figure out of any effect of antibody to reduce the impact of PAF and LPS. Main rat trial 2 resulted no significant symptoms like characteristic acute diarrhea and weight loss of colitis. This study suggested that colostrums from sheep immunized against TNF-${\alpha}$ significantly inhibited TNF-${\alpha}$ bioactivity in the cell based assay. And the higher than anticipated variability in the two animal models precluded assessment of the ability of antibody to prevent TNF-${\alpha}$ induced intestinal damage in the intact animal. Further study will require to find out an alternative animal model, which is more acceptable to test anti-TNF-${\alpha}$ IgA therapy for reducing the impact of inflammation on gut dysfunction. And subsequent pre-clinical and clinical testing also need generation of more antibody as current supplies are low.

• Journal of Sasang Constitutional Medicine
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• v.8 no.1
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• pp.247-294
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• 1996

By means of the statistical data which has been collected with newly revised QSCC made use of the outpatient group examined at Kyung-Hee Medical Center and an open ordinary person group, the author proceeded statistical analysis for the validation study of the revised questionnaire itself. First, check the accurate discrimination rate by performing discriminant analysis on the statistical data of the patient group. And next, sought T-score by applying the norms gained in process of standadization of the open ordinary person group to the Sasang scale score of the outpatient group and investigated the distinctive feature between the subpopulations which was devided in the process of multivarite cluster analysis. The result was summarized as follows ; 1. The validity of the questionnaire was established through the fact that the accurate discrimination rate the ratio between predicted group and actual group was figured out 70.08%. 2. At the profile analysis the response to the relevant scale showed notable upward tendency in each constitutional group and therefore it seems to be pertinent in the field of constitutional discrimination. 3. In the observation of the power of expression through the profile analysis of each constitutional group the Soyang group demonstrated the most remarkable outcome, the Soeum group was the most inferior and the Taieum group revealed a sort of dual property. 4. What is called the group of seceder out of three subpopulation of each constitutional group distinguished definitely from the contrasted groups at the point of the distinctive profile feature and the content is like following description. (1) The seceder group of Soyang-in showed considerably passive disposition differently from general character of ordinary Soyang group and an appearance attracting the attention is that they demonstrated comparatively higher response at Soeum scale (2) The seceder group of Taieum-in gained low scores in general that informed the passive disposition of the group and the other way of the general property of Taieum group which showed accompanied ascension in Taiyang-Taieum scales they demonstrated sharply declined score at Taiyang scale (3) The seceder group of Soeum-in demonstrated distinctive property similar to the profile feature of Soyang group and it notifies that the passive property of Soeum group was diluted for the most part. According to the above result, the validity of newly revised questionnaire has been proven successfully and the property of seceder groups could be noticed to some degree through the profile analysis on the course of this study. The result of this study is expected to use as a research materials to produce next edition of the questionnaire and it is regarded that further inquisition about the difference between the seceder group and the contrasted group is required for the promotion of the questionnaire as it refered several times in the contents of the main discourse.

• 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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.193-216
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• 2020

Due to the recent mid- to long-term slump and falling growth rates in the global economy, interest in organizational structures that create new products or services as a new alternative to survive and develop in an opaque environment both internally and externally, and enhance organizational sustainability through changes in production methods and business innovation is increasing day by day. In this atmosphere, we agree that the growth of small and medium-sized venture companies has a significant impact on the national economy, and various efforts are being made to enhance the technological innovation capabilities of the members so that these small and medium-sized venture companies can enhance and sustain their performance. The purpose of this study is also to investigate how the technological innovation capabilities of small and medium-sized venture companies correlate with the performance of knowledge management and to analyze the role of network capabilities to organize the strategic activities of enterprise to obtain the resources and organizational capabilities to be used for value creation from external networks. In other words, research was conducted on the impact of technological innovation capabilities of small and medium venture companies on knowledge management performance by using network capabilities as parameters. Therefore, in this study, we would like to verify the hypothesis that innovation capabilities will have a positive impact on knowledge management performance by using network capabilities of small and medium venture companies. Economic activities based on technological innovation capabilities should respond quickly to new changes in an environment where uncertainty has increased, and lead to macro-economic growth and development as well as overcoming long-term economic downturns so that they can become the nation's new growth engine as well as sustainable growth and survival of the organization. In addition, this study was conducted by setting the most important knowledge management performance within the organization as a dependent variable. As a result, R&D and learning capabilities among technological innovation capabilities have no impact on financial performance. In contrast, it was shown that corporate innovation activities have a positive impact on both financial and non-financial performance. The fact that non-financial factors such as quality and productivity improvement are identified in the management of small and medium-sized venture companies utilizing their technological innovation capabilities is contrary to a number of studies by those corporate innovation activities affect financial performance during prior research. The reason for this result is that research companies have been out of start-up companies for more than seven years, but sales are less than 10 billion won, and unlike start-up companies, R&D and learning capabilities have more positive effects on intangible non-financial performance than financial performance. Corporate innovation activities have been shown to have a positive (+) impact on both financial and non-financial performance, while R&D and learning capabilities have a positive (+) impact on financial performance by parameters of network capability. Corporate innovation activities have been shown to have no impact on both financial and non-financial performance, and R&D and learning capabilities have no impact on non-financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance.

• Radiation Oncology Journal
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• v.16 no.2
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• pp.147-157
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• 1998

Purpose : The aim of this retrospective study is to assess the necessity of s1aging laparotomy in the management of supradiaphragmatic CS I-II Hodgkin's disease. Prognostic factors and the usefulness of prognostic factor groups were also analyzed. Materials and Methods : From 1985 to 1995, fifty one Patients who were diagnosed as supradiaphragmatic CS I-II Hodgkin's disease at Yonsei Cancer Center in Seoul, Korea were enrolled in this study Age range was 4 to 67 with median age of 30. The number of patients with each CS IA, II A, and IIB were 16, 25, and 10, respectively. Radiotherapy(RT) was delivered using 4 or 6 MV photon beam to a total dose of 19.5 to 55.6Gy (median dose : 45Gy) with a 1.5 to 1.BGy per fraction. Chemotherapy(CT) was given in 2-12 cycles(median : 6 cycles). Thirty one Patients were treated with RT alone, 4 patients with CT alone and 16 patients with combined chemoradiotherapy. RT volumes varied from involved fields(3), subtotal nodal fields(18) or mantle fields(26). Results : Five-year disease-free survival rate(DFS) was $78.0\%$ and overall survival rate(05) was $87.6\%$. Fifty Patients achieved a complete remission after initial treatment and 8 patients were relapsed. Salvage therapy was given to 7 patients, 1 with RT alone, 4 with CT alone, 2 with RT+CT. Only two patients were successfully salvaged. Feminine gender and large media-stinal adenopathy were significant adverse prognostic factors in the univariate analysis for DFS. The significant adverse prognostic factors of OS were B symptom and clinical stage. When patients were analyzed according to European Organization for Research and Treatment of Cancer(EORTC) prognostic factor groups, the DFS in Patients with very favorable, favorable and unfavorable group was 100, 100 and $55.8\%$(p<0.05), and the 05 in each patients' group was 100, 100 and $75.1\%$(p<0.05), respectively. In very favorable and favorable groups, the DFS and 05 were all $100\%$ by RT alone, but in unfavorable group, RT with CT had a lesser relapse rate than RT alone. The subtotal nodal irradiation had better OFS than mantle RT in patients treated with RT. Conclusion : In present study, the DFS and OS in patients who did not undergo s1aging laparotomy were similar with the results in the literatures of which patients were surgically staged. Therefore, we may suggest that staging laparotomy would not influence the outcome of treatments. In univariate analysis, gender, large mediastinal adenopathy. B symptoms and clinical stage were significant prognostic factors for the survival rate. We confirm the usefulness of EORTC prognostic factor groups which may be a good.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• The Korean Journal of Nuclear Medicine Technology
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• v.20 no.2
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• pp.62-68
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• 2016

Purpose In a cyclosporine experiment using a robotic liquid handing system has found a deviation of its standard curve and low reproducibility of patients's results. The difference of the test is that methanol is mixed with samples and the extractions are used for the test. Therefore, we assumed that the abnormal test results came from using methanol and conducted this test. In a manual of a robotic liquid handling system mentions that we can choose several setting parameters depending on the viscosity of the liquids being used, the size of the sampling tips and the motor speeds that you elect to use but there's no exact order. This study was undertaken to confirm pipetting ability depending on types of liquids and investigate proper setting parameters for the optimum dispensing ability. Materials and Methods 4types of liquids(water, serum, methanol, PEG 6000(25%)) and $TSH^{125}I$ tracer(515 kBq) are used to confirm pipetting ability. 29 specimens for Cyclosporine test are used to compare results. Prepare 8 plastic tubes for each of the liquids and with multi pipette $400{\mu}l$ of each liquid is dispensed to 8 tubes and $100{\mu}l$ of $TSH^{125}I$ tracer are dispensed to all of the tubes. From the prepared samples, $100{\mu}l$ of liquids are dispensed using a robotic liquid handing system, counted and calculated its CV(%) depending on types of liquids. And then by adjusting several setting parameters(air gap, dispense time, delay time) the change of the CV(%)are calcutated and finds optimum setting parameters. 29 specimens are tested with 3 methods. The first(A) is manual method and the second(B) is used robotic liquid handling system with existing parameters. The third(C) is used robotic liquid handling system with adjusted parameters. Pipetting ability depending on types of liquids is assessed with CV(%). On the basis of (A), patients's test results are compared (A)and(B), (A)and(C) and they are assessed with %RE(%Relative error) and %Diff(%Difference). Results The CV(%) of the CPM depending on liquid types were water 0.88, serum 0.95, methanol 10.22 and PEG 0.68. As expected dispensing of methanol using a liquid handling system was the problem and others were good. The methanol's dispensing were conducted by adjusting several setting parameters. When transport air gap 0 was adjusted to 2 and 5, CV(%) were 20.16, 12.54 and when system air gap 0 was adjusted to 2 and 5, CV(%) were 8.94, 1.36. When adjusted to system air gap 2, transport air gap 2 was 12.96 and adjusted to system air gap 5, Transport air gap 5 was 1.33. When dispense speed was adjusted 300 to 100, CV(%) was 13.32 and when dispense delay was adjusted 200 to 100 was 13.55. When compared (B) to (A), the result increased 99.44% and %RE was 93.59%. When compared (C-system air gap was adjusted 0 to 5) to (A), the result increased 6.75% and %RE was 5.10%. Conclusion Adjusting speed and delay time of aspiration and dispense was meaningless but changing system air gap was effective. By adjusting several parameters proper value was found and it affected the practical result of the experiment. To optimize the system active efforts are needed through the test and in case of dispensing new types of liquids proper test is required to check the liquid is suitable for using the equipment.