• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.

• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.35-42
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• 2016

Recently many researches have been focused on the use of the markerless augmented reality system using face, foot, and hand of user's body to alleviate many disadvantages of the marker based augmented reality system. In addition, most existing augmented reality systems have been utilized rigid objects since they just desire to insert and to basic interaction with virtual object in the augmented reality system. In this paper, unlike restricted marker based augmented reality system with rigid objects that is based in display, we designed and implemented the markerless augmented reality system using deformable objects to apply various fields for interactive situations with a user. Generally, deformable objects can be implemented with mass-spring modeling and the finite element modeling. Mass-spring model can provide a real time simulation and finite element model can achieve more accurate simulation result in physical and mathematical view. In this paper, the proposed markerless augmented reality system utilize the mass-spring model using tetraheadron structure to provide real-time simulation result. To provide plausible simulated interaction result with deformable objects, the proposed method detects and tracks users hand with Kinect SDK and calculates the external force which is applied to the object on hand based on the position change of hand. Based on these force, 4th order Runge-Kutta Integration is applied to compute the next position of the deformable object. In addition, to prevent the generation of excessive external force by hand movement that can provide the natural behavior of deformable object, we set up the threshold value and applied this value when the hand movement is over this threshold. Each experimental test has been repeated 5 times and we analyzed the experimental result based on the computational cost of simulation. We believe that the proposed markerless augmented reality system with deformable objects can overcome the weakness of traditional marker based augmented reality system with rigid object that are not suitable to apply to other various fields including healthcare and education area.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.2
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• pp.3-9
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• 2013

Purpose: Currently commercially available phantom can reproduce and evaluate only a static situation, the study is incomplete research on phantom and system which is can confirmed functional situation in the kidney by time through dynamic phantom and blood flow velocity, various difference according to the amount of radioactive. Therefore, through this study, it has produced the dynamic kidney phantom to reproduce images through the dynamic flow of the kidney, it desire to evaluate the usefulness of nuclear medicine imaging. Materials and Methods: The production of the kidney phantom was fabricated based on the normal adult kidney, in order to reproduce the dynamic situation based on the fabricated kidney phantom, in this study it was applied the volume pump that can adjust the speed of blood flow, so it can be integrated continuously radioactive isotopes in the kidney by using $^{99m}Tc-pertechnate$. Used the radioactive isotope was supplied through the two pump. It was confirmed the changes according to the infusion rate, radioactive isotopes and the different injection speeds on the left and right, analysis of the acquired images was done by drawn ten times ROI in order to check the reproducibility of each on the front and rear of the kidney and bladder. Results: Under the same conditions infusion rate 40 mL/min fixed to adjust the pressure of the pump when the radiopharmaceuticals between 2-3 minutes in the most integrated in the kidney phantom was excreted inthe bladder. Glomerular filtration rate (GFR), respectively, by each device SYMBIA 1,091 mL/min, FORTE 1,232 mL/min, ARGUS 1,264 mL/min, INFINIA 1,302 mL/min in that there isno statistically significant difference was found, Tmax values and T1/2 values stars from all equipment with no statistically significant difference was found. CV values of the coefficient of variation less than 5% was found to be repeatable, and to 2.67% of the lowest SYMBIA appeared, INFINIA was the highest in the 4.86%. Conclusion: Through this study, the results showed that the dynamic kidney phantom system is able to similarly reproduce renogram in the actual clinical. Especially, the depicted over time for the flow to be excreted through the kidney into the bladder was adequately reproduce, it is expected to be utilized as basic data to check the quality of the dynamic images. In addition, it is considered to help in the field of functional imaging and quality control.

• Journal of radiological science and technology
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• v.32 no.3
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• pp.325-334
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• 2009

Purpose : This study was conducted to assess how effective the permeability ratio and relative cerebral blood volume ratio are to tumor through perfusion MRI by measuring and reflecting the grade assessment and differential diagnosis and the permeability and relative cerebral blood volume of contrast media plunged from blood vessel into organ due to breakdown of blood-brain barrier in cerebral. Subject and Method : Subject of study was 29 patients whose diagnosis were confirmed by biopsy after surgery and 550 (11 slice$\times$50 image) perfusion MRI were used to make image of relative cerebral blood volume with the program furnished on instrument. The other method was to transmit to private computer and the image analysis was made additionally by making image of relative cerebral blood volume-reformulated singular value decomposition, rCBV-rSVD and permeability using IDL.6.2. In addition, Kruskal-wallis test tonggyein non numerical average by a comparative analysis of brain tumors Results : The rCBV ratio (Functool PF; GE Medical Systems and IDL 6.2 program by analysis) and permeability ratio of tumors were as follows; high grade glioma(n=4), (14.75, 19.25) 13.13. low grade astrocytoma(n=5) (14.80, 15.90) 11.60, glioblastoma(n=5) (10.90, 18.60), 22.00, metastasis(n=6) (11.00, 15.08). 22.33. meningioma(n=6) (18.58, 7.67), 5.58. oliogodendroglioma(n=3) (23.33, 16.33, 15.67. Conclusion : It was not easy to classify the grade with the relative cerebral blood volume ratio measured by using the relative cerebral blood image by type of tumors, however, permeability ratio measured by permeability image revealed that the higher the grade of tumor, the higher the measured permeability ratio, showing the assessment of tumor grade is more effective to differential diagnosis.

• The Korean Journal of Nuclear Medicine
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• v.39 no.3
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• pp.174-181
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• 2005

Purpose: Reduction of respiratory motion artifacts in PET images was studied using respiratory-gated PET (RGPET) with moving phantom. Especially a method of generating simulated helical CT images from 4D-CT datasets was developed and applied to a respiratory specific RGPET images for more accurate attenuation correction. Materials and Methods: Using a motion phantom with periodicity of 6 seconds and linear motion amplitude of 26 mm, PET/CT (Discovery ST: GEMS) scans with and without respiratory gating were obtained for one syringe and two vials with each volume of 3, 10, and 30 ml respectively. RPM (Real-Time Position Management, Varian) was used for tracking motion during PET/CT scanning. Ten datasets of RGPET and 4D-CT corresponding to every 10% phase intervals were acquired. from the positions, sizes, and uptake values of each subject on the resultant phase specific PET and CT datasets, the correlations between motion artifacts in PET and CT images and the size of motion relative to the size of subject were analyzed. Results: The center positions of three vials in RGPET and 4D-CT agree well with the actual position within the estimated error. However, volumes of subjects in non-gated PET images increase proportional to relative motion size and were overestimated as much as 250% when the motion amplitude was increased two times larger than the size of the subject. On the contrary, the corresponding maximal uptake value was reduced to about 50%. Conclusion: RGPET is demonstrated to remove respiratory motion artifacts in PET imaging, and moreover, more precise image fusion and more accurate attenuation correction is possible by combining with 4D-CT.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.