• Korean Journal of Clinical Laboratory Science
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• v.37 no.2
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• pp.65-70
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• 2005

Those specifications of the CLIA analytical tolerance limits are consistent with the performance goals in Six Sigma Quality Management. Six sigma analysis determines performance quality from bias and precision statistics. It also shows if the method meets the criteria for the six sigma performance. Performance standards calculates allowable total error from several different criteria. Six sigma means six standard deviations from the target value or mean value and about 3.4 failures per million opportunities for failure. Sigma Quality Level is an indicator of process centering and process variation total error allowable. Tolerance specification is replaced by a Total Error specification, which is a common form of a quality specification for a laboratory test. The CLIA criteria for acceptable performance in proficiency testing events are given in the form of an allowable total error, TEa. Thus there is a published list of TEa specifications for regulated analytes. In terms of TEa, Six Sigma Quality Management sets a precision goal of TEa/6 and an accuracy goal of 1.5 (TEa/6). This concept is based on the proficiency testing specification of target value +/-3s, TEa from reference intervals, biological variation, and peer group median mean surveys. We have found rules to calculate as a fraction of a reference interval and peer group median mean surveys. We studied to develop total error allowable from peer group survey results and CLIA 88 rules in US on 19 items TP, ALB, T.B, ALP, AST, ALT, CL, LD, K, Na, CRE, BUN, T.C, GLU, GGT, CA, phosphorus, UA, TG tests in chematology were follows. Sigma level versus TEa from peer group median mean CV of each item by group mean were assessed by process performance, fitting within six sigma tolerance limits were TP ($6.1{\delta}$/9.3%), ALB ($6.9{\delta}$/11.3%), T.B ($3.4{\delta}$/25.6%), ALP ($6.8{\delta}$/31.5%), AST ($4.5{\delta}$/16.8%), ALT ($1.6{\delta}$/19.3%), CL ($4.6{\delta}$/8.4%), LD ($11.5{\delta}$/20.07%), K ($2.5{\delta}$/0.39mmol/L), Na ($3.6{\delta}$/6.87mmol/L), CRE ($9.9{\delta}$/21.8%), BUN ($4.3{\delta}$/13.3%), UA ($5.9{\delta}$/11.5%), T.C ($2.2{\delta}$/10.7%), GLU ($4.8{\delta}$/10.2%), GGT ($7.5{\delta}$/27.3%), CA ($5.5{\delta}$/0.87mmol/L), IP ($8.5{\delta}$/13.17%), TG ($9.6{\delta}$/17.7%). Peer group survey median CV in Korean External Assessment greater than CLIA criteria were CL (8.45%/5%), BUN (13.3%/9%), CRE (21.8%/15%), T.B (25.6%/20%), and Na (6.87mmol/L/4mmol/L). Peer group survey median CV less than it were as TP (9.3%/10%), AST (16.8%/20%), ALT (19.3%/20%), K (0.39mmol/L/0.5mmol/L), UA (11.5%/17%), Ca (0.87mg/dL1mg/L), TG (17.7%/25%). TEa in 17 items were same one in 14 items with 82.35%. We found out the truth on increasing sigma level due to increased total error allowable, and were sure that the goal of setting total error allowable would affect the evaluation of sigma metrics in the process, if sustaining the same process.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.2
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• pp.161-169
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• 2016

Purpose : To quantify the inter-fractional variation in prostate displacement and their dosimetric effects for prostate cancer treatment. Materials and Methods : A total of 176 daily cone-beam CT (CBCT) sets acquired for 6 prostate cancer patients treated with volumetric-modulated arc therapy (VMAT) were retrospectively reviewed. For each patient, the planning CT (pCT) was registered to each daily CBCT by aligning the bony anatomy. The prostate, rectum, and bladder were delineated on daily CBCT, and the contours of these organs in the pCT were copied to the daily CBCT. The concordance of prostate displacement, deformation, and size variation between pCT and daily CBCT was evaluated using the Dice similarity coefficient (DSC). Results : The mean volume of prostate was 37.2 cm3 in the initial pCT, and the variation was around ${\pm}5%$ during the entire course of treatment for all patients. The mean DSC was 89.9%, ranging from 70% to 100% for prostate displacement. Although the volume change of bladder and rectum per treatment fraction did not show any correlation with the value of DSC (r=-0.084, p=0.268 and r=-0.162, p=0.032, respectively), a decrease in the DSC value was observed with increasing volume change of the bladder and rectum (r=-0.230,p=0.049 and r=-0.240,p=0.020, respectively). Conclusion : Consistency of the volume of the bladder and rectum cannot guarantee the accuracy of the treatment. Our results suggest that patient setup with the registration between the pCT and daily CBCT should be considered aligning soft tissue.

• Korean Journal of Cognitive Science
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• v.28 no.1
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• pp.1-26
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• 2017

This study investigated the cortical generators of P300 in college students with schizotypal traits by using an auditory oddball paradigm, event-related potentials (ERPs) and standardized low resolution brain electromagnetic tomography (sLORETA) model. We also investigated the relationship between the current density of P300 and the clinical symptoms of schizophrenia. Based on the scores of Schizotypal Personality Questionnaire(SPQ), schizotypal trait (n=37) and control (n=42) groups were selected. For the measurement of P300, an auditory oddball paradigm, in which frequent standard tones (1000Hz) and rare target tones (1500Hz) were presented randomly, was used. Participants were required to count the number of the target tones during the task and report this at the end of the experiment. The two groups did not differ significantly in the accuracy of the oddball task. The schizotypal trait group showed significantly smaller P300 amplitudes than control group. In terms of source localization, both groups showed the P300 current density over bilateral frontal, parietal, temporal and occipital lobes. However, the schizotypal trait group showed significantly reduced activations in the left superior temporal gyrus and the right middle temporal gyrus, but increased activations in both left inferior frontal gyrus and right superior frontal gyrus compared to the control group. Furthermore, a negative correlation between the current density of the right superior frontal gyrus and SPQ disorganization score was found in the schizotypal trait group. These findings indicate that the individuals with schizotypal traits have dysfunctions of frontal and temporal areas, which are known to be the source of P300, as observed in patients with schizophrenia. In addition, the present results indicate that the disorganization score, rather than total score, of the SPQ is useful in predicting the risk of future schizophrenia.

• Korean Journal of Digital Imaging in Medicine
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• v.16 no.1
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• pp.13-19
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• 2014

Purpose: FLAIR image is beneficial for the diagnosis of various bran diseases including ischemic CVS, brain tumors and infections. However the border between the legion of brain metastasis and surrounding edema may not be clear. Therefore, this study aims to investigate the practical benefits of delayed imaging by comparing the image from a patient with brain metastasis before a contrast enhancement and the image 10 minutes after a contrast enhancement. Materials and methods: Of the 92 people who underwent MRI brain metastases in suspected patients 13 people in three patients there is no video to target the 37 people confirmed cases, and motion artifacts brain metastases in our hospital June-December 2013, 18 people measurement position except for the three incorrect patient (male: 11 people, female: 7 people, average age: 60 years) in the target, test equipment, 3.0T MR System (ACHIEVA Release, Philips, I was 8ChannelSENSE Head Coil use Best, and the Netherlands). TR 11000 ms, TE 125 ms, TI2800 ms, Slice Thickness 5 mm, gap 5 mm, is a Slice number 21, the parameters of the 3D FFE, T2 FLAIR variable that was used to test, TR 8.1 ms, TE 3.7 ms, Slice number 240 I set to. The experiment was conducted by acquiring the FLAIR prior to contrast enhancement (heretofore referred to as Pre FLAIR), and acquiring the 3D FFE CE five minutes after the contrast enhancement, and recomposing the images in an axial plane of S/T 3mm, G 0mm (heretofore referred to as MPR TRA CE). Using the FLAIR 10 minutes after the contrast enhancement (heretofore referred to as Post FLAIR) and Pi-View, a retrospective study was conducted. Using MRIcro on the image of a patient confirmed for his diagnosis, the images before and after the contrast media, as well as the CNR and SNR of the MPR TRA CE images of the lesion and the site absent of lesion were compared and analyzed using a one-way analysis of variance. Results: CNR for Pre FLAIR and Post FLAIR were 34.35 and 60.13, respectively, with MPR TRA CE at 23.77 showing no significant difference (p<0.050). Post-experiment analysis shows a difference between Pre FLAIR and Post FLAIR in terms of CNR (p<0.050), but no difference in CNR between Post FLAIR and MPR TRA CE (p>0.050), indicating that the contrast media had an effect only on Pre FLAIR and Post FLAIR. The SNR for the normal site Pre FLAIR was 106.43, and for the lesion site 140.79. Post FLAIR for the normal site was 107.79, and for the lesion site 167.91. MPR TRA CE for the normal site was 140.23 and for the lesion site 183.19, showing significant difference (p<0.050), and post-experiment analysis shows that there was a difference in SNR only on the lesion sites for Pre FLAIR and Post FLAIR (p<0.050). There was no difference in SNR between the normal site and lesion site for Post FLAIR and MPR TRA CE, indicating no effect from the contrast media (p>0.050). Conclusions: This experiment shows that Post FLAIR has a higher contrast than Pre FLAIR, and a higher SNR for lesions, It was not not statistically significant and MPR TRA CE but CNR came out high. Inspection of post-contrast which is used in a high magnetic field is frequently used images of 3D T1 but, since the signal of the contrast medium and the blood flow is included, this method can be diagnostic accuracy is reduced, it is believed that when used in combination with Post FLAIR, and that can provide video information added to the diagnosis of brain metastases.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.195-211
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• 2010

An exhibition is defined as market events for specific duration to present exhibitors' main product range to either business or private visitors, and it also plays a key role as effective marketing channels. Especially, as the effect of the opinions of the visitors after the exhibition impacts directly on sales or the image of companies, exhibition organizers must consider various needs of visitors. To meet needs of visitors, ubiquitous technologies have been applied in some exhibitions. However, despite of the development of the ubiquitous technologies, their services cannot always reflect visitors' preferences as they only generate information when visitors request. As a result, they have reached their limit to meet needs of visitors, which consequently might lead them to loss of marketing opportunity. Recommendation systems can be the right type to overcome these limitations. They can recommend the booths to coincide with visitors' preferences, so that they help visitors who are in difficulty for choices in exhibition environment. One of the most successful and widely used technologies for building recommender systems is called Collaborative Filtering. Traditional recommender systems, however, only use neighbors' evaluations or behaviors for a personalized prediction. Therefore, they can not reflect visitors' dynamic preference, and also lack of accuracy in exhibition environment. Although there is much useful information to infer visitors' preference in ubiquitous environment (e.g., visitors' current location, booth visit path, and so on), they use only limited information for recommendation. In this study, we propose a booth recommendation methodology using Sequential Association Rule which considers the sequence of visiting. Recent studies of Sequential Association Rule use the constraints to improve the performance. However, since traditional Sequential Association Rule considers the whole rules to recommendation, they have a scalability problem when they are adapted to a large exhibition scale. To solve this problem, our methodology composes the confidence database before recommendation process. To compose the confidence database, we first search preceding rules which have the frequency above threshold. Next, we compute the confidences of each preceding rules to each booth which is not contained in preceding rules. Therefore, the confidence database has two kinds of information which are preceding rules and their confidence to each booth. In recommendation process, we just generate preceding rules of the target visitors based on the records of the visits, and recommend booths according to the confidence database. Throughout these steps, we expect reduction of time spent on recommendation process. To evaluate proposed methodology, we use real booth visit records which are collected by RFID technology in IT exhibition. Booth visit records also contain the visit sequence of each visitor. We compare the performance of proposed methodology with traditional Collaborative Filtering system. As a result, our proposed methodology generally shows higher performance than traditional Collaborative Filtering. We can also see some features of it in experimental results. First, it shows the highest performance at one booth recommendation. It detects preceding rules with some portions of visitors. Therefore, if there is a visitor who moved with very a different pattern compared to the whole visitors, it cannot give a correct recommendation for him/her even though we increase the number of recommendation. Trained by the whole visitors, it cannot correctly give recommendation to visitors who have a unique path. Second, the performance of general recommendation systems increase as time expands. However, our methodology shows higher performance with limited information like one or two time periods. Therefore, not only can it recommend even if there is not much information of the target visitors' booth visit records, but also it uses only small amount of information in recommendation process. We expect that it can give real?time recommendations in exhibition environment. Overall, our methodology shows higher performance ability than traditional Collaborative Filtering systems, we expect it could be applied in booth recommendation system to satisfy visitors in exhibition environment.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.526-535
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• 2021

Spatial sampling design plays an important role in GIS-based modeling studies because it increases modeling efficiency while reducing the cost of sampling. In the field of agricultural systems, research demand for high-resolution spatial databased modeling to predict and evaluate climate change impacts is growing rapidly. Accordingly, the need and importance of spatial sampling design are increasing. The purpose of this study was to design spatial sampling of paddy fields (11,386 grids with 1 km spatial resolution) in Korea for use in agricultural spatial modeling. A stratified random sampling design was developed and applied in 2030s, 2050s, and 2080s under two RCP scenarios of 4.5 and 8.5. Twenty-five weather and four soil characteristics were used as stratification variables. Stratification and sample allocation were optimized to ensure minimum sample size under given precision constraints for 16 target variables such as crop yield, greenhouse gas emission, and pest distribution. Precision and accuracy of the sampling were evaluated through sampling simulations based on coefficient of variation (CV) and relative bias, respectively. As a result, the paddy field could be optimized in the range of 5 to 21 strata and 46 to 69 samples. Evaluation results showed that target variables were within precision constraints (CV<0.05 except for crop yield) with low bias values (below 3%). These results can contribute to reducing sampling cost and computation time while having high predictive power. It is expected to be widely used as a representative sample grid in various agriculture spatial modeling studies.

• Korean Journal of Cognitive Science
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• v.34 no.2
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• pp.111-151
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• 2023

It is reported that binge drinkers show cognitive impairment similar to alcohol use disorder patients. A previous studies using neuropsychological tests and brain imaging techniques to investigate the visual perception of alcohol use disorder patients reported that they had global-local visual perception defects. Although the neurological basis for the global-local visual perception deficit in the heavy drinking group has been presented, there are no studies to date that have investigated the global-local visual perception in the heavy drinking group. This study investigated local-biased visual perception in female college students with binge drinking (BD) using event-related potentials (ERPs). Based on the scores of the Korean version of Alcohol Use Disorder Identification Test and the Alcohol Use Questionnaire, participants were assigned into BD (n=25) and non-BD (n=25) groups. Local-global visual processing was assessed using a local-global paradigm, in which large stimuli (global level) composed of small stimuli (local level) were presented. The stimuli presented at global and local levels were either congruent or incongruent. The behavioral results exhibited that the BD and non-BD groups did not differ in terms of accuracy and response time. In terms of ERPs, the BD and non-BD groups did not show difference in N100, P150 and N200 amplitude. However, the BD group showed significantly smaller P300 amplitude than non-BD group especially in the local condition. In addition, a negative correlation between P300 amplitude and binge drinking score was observed, i.e., severer binge drinking smaller P300 amplitude. The P300 is known to reflect cognitive inhibition and attentional allocation. In the global-local paradigm, the local condition required to attend to local target while ignoring global non-target. Therefore, the present results indicate that female college students with BD do not have local-biased visual processing, instead they seem to have difficulties in inhibition of irrelevant stimuli.

• Science of Emotion and Sensibility
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• v.13 no.3
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• pp.541-550
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• 2010

The present study examined whether the physical similarity of test stimuli affects P300 amplitude and detection accuracy for the P300-based concealed information test (P300 CIT). As the participant pretended suffering from memory impairment by an accident, own name was used as a concealed information to be probed by the P300 CIT in which the participant discriminated between a target and other (probe, irrelevant) stimuli. One group of participants was tested in the easy task condition with low physical similarity among stimuli, the other group was tested in the difficult task condition with high physical similarity among stimuli. Using the base-to-peak P300 amplitude, the interaction effect of task difficulty and stimulus type was significant at $\alpha$=.1 level (p=.052). In the easy task condition the difference of P300 amplitude between the probe and the irrelevant stimuli was significant, while in the difficult task condition the difference was not significant. Using peak-to-peak P300 amplitude, on the other hand, the interaction effect of task difficulty and stimulus type was not significant with significant differences of P300 amplitude between the probe and the irrelevant stimuli in both task difficulty conditions. The difference of detection accuracy between task conditions was not significant with both measures of P300 amplitude although the difference was much smaller when peak-to-peak P300 amplitude was used. The results suggest that the efficiency of P300 CIT would not decrease even when the perceptual similarity among test stimuli is high.