• Restorative Dentistry and Endodontics
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• v.35 no.6
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• pp.453-460
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• 2010

Objectives: To evaluate the accuracy and consistency of two different apex locators at both the Apex and 0.5 marks. Materials and Methods: Twenty-six root canals was scheduled for extraction for periodontal or prosthodontic reasons. Thirteen canals were measured using Root ZX and the rest by i-ROOT. The root canal length was measured both the at 0.5 mark and the Apex mark. The file was then fixed to the toot, and the distance from the file tip to the major foramen of each canal was measured after removing the root dentin under the microscope so that the major foramen and the file tip were seen. Results: 1. When the Apex mark was used, 100% of both the Root ZX and i-ROOT groups were within 0.5 mm of the major foramen. 2. When 0.5 mark was used, 100% of the Root ZX group and 77% of the i-ROOT group were within 0.5 mm of the major foramen. 3. In terms of standard deviation and quartile value, the Apex mark was more consistent than 0.5 mark in the Root ZX group, and 0.5 mark was more consistent in the i-ROOT group, but there was no statistically significant difference when compared with t-test. 4. The root canal length difference between the Apex mark and 0.5 mark was 0.22 mm and 0.46 mm in the Root ZX and i-ROOT groups, respectively. Conclusions: In this study, the Apex mark was the more consistent mark. Therefore, it is recommended to subtract 0.5 mm, which is the average length between the apex and apical constriction, from the root canal length at the Apex mark to obtain the working length clinically.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1559-1572
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• 2021

The response of vegetation under the crop stress condition was evaluated using structural, biochemical, and physiological vegetation indices based on unmanned aerial vehicle (UAV) images and field-spectrometer data. A high concentration of herbicide was sprayed at the different growth stages of garlic to process crop stress, the above ground dry matter of garlic at experimental area (EA) decreased about 46.2~84.5% compared to that at control area. The structural vegetation indices clearly responded to these crop damages. Spectral reflectance at near-infrared wavelength consistently decreased at EA. Most biochemical vegetation indices reflected the crop stress conditions, but the meaning of physiological vegetation indices is not clear due to the effect of vinyl mulching. The difference of the decreasing ratio of vegetation indices after the herbicide spray was 2.3% averagely in the case of structural vegetation indices and 1.3~4.1% in the case of normalization-based vegetation indices. These results meant that appropriate vegetation indices should be utilized depending on the types of crop stress and the cultivation environment and the normalization-based vegetation indices measured from the different spatial scale has the minimized difference.

• Journal of the Korean Applied Science and Technology
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• v.36 no.3
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• pp.995-1007
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• 2019

The purpose of this study was to investigate the effects of 12-week elastic band exercise on body composition, blood lipids and AMPK in 24 elderly female volunteers aged 65-75 years, and they were divided into the combined exercise group(n=12) and the control group(n=12). The elastic band exercise method was to do exercise 3 times a week for 60 minutes per session, 1-4 weeks for low intensity of OMNI-RES 3-4, 5-8 weeks for medium intensity of OMNI-RES 5-6, 9-12 weeks for OMNI-RES 7-8 of high intensity. In order to compare the differences in the groups before and after the elastic band exercise, two-way repeated measures ANOVA was used to verify the interaction between group and time. The difference in the groups of the measured data was paired t-test, the difference between the groups was paired independent t-test, and analysis of covariance ANCOVA was performed to minimize the inter-group error. The statistical significance level of each item was set to .05. As a result, body fat percentage of exercise group significantly decreased (p<.05), and skeletal muscle volume was significantly increased (p<.01). TC, TG and LDL-C were not significantly different between the exercise and control groups, and HDL-C was significantly decreased in the control group (p<.05). AMPK was significantly decreased in the exercise group (p<.001), but there was no significant difference in the control group. According to the covariance analysis to minimize the error of difference between the pre-exercise groups (p<.05), there was significant difference in AMPK of groups after exercise. These results suggest that the 12-week elastic band exercise has a positive effect on the body composition and AMPK of the elderly women.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

• The Journal of Korean Society for Radiation Therapy
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• v.25 no.2
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• pp.115-121
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• 2013

Purpose: Latest linear accelerator and the introduction of new measurement equipment to the agency that the introduction of this equipment in the future, by analyzing the process of confirming the usefulness of the preparation process for applying it in the clinical causes some problems, should be helpful. Materials and Methods: All measurements TrueBEAM STX (Varian, USA) was used, and a file specific to each energy, irradiation conditions, the dose distribution was calculated using a computerized treatment planning equipment (Eclipse ver 10.0.39, Varian, USA). Measuring performance and cause errors in MapCHECK 2 were analyzed and measured against. In order to verify the performance of the MapCHECK 2, 6X, 6X-FFF, 10X, 10X-FFF, 15X field size $10{\times}10$ cm, gantry $0^{\circ}$, $180^{\circ}$ direction was measured by the energy. IGRT couch of the CT values affect the measurements in order to confirm, CT number values : -800 (Carbon) & -950 (COUCH in the air), -100 & 6X-950 in the state for FFF, 15X of the energy field sizes $10{\times}10$, gantry $180^{\circ}$, $135^{\circ}$, $275^{\circ}$ directionwas measured at, MapPHAN allocated to confirm the value of HU were compared, using the treatment planning computer for, Measurement error problem by the sharp edges MapPHAN Learn gantry direction MapPHAN of dependence was measured in three ways. GANTRY $90^{\circ}$, $270^{\circ}$ in the direction of the vertically erected settings 6X-FFF, 15X respectively, and Setting the state established as a horizontal field sizes $10{\times}10$, $90^{\circ}$, $45^{\circ}$, $315^{\circ}$, $270^{\circ}$ of in the direction of the energy-6X-FFF, 15X, respectively, were measured. Without intensity modulated beam of the third open arc were investigated. Results: Of basic performance MapCHECK confirm the attenuation measured by Couch, measured from the measured HU values that are assigned to the MAP-PHAN, check for calculation accuracy for the angled edge of the MapPHAN all come in a range of valid measurement errors do not affect the could see. three ways for the Gantry direction dependence, the first of the meter built into the value of the Gantry $270^{\circ}$ (relative $0^{\circ}$), $90^{\circ}$ (relative $180^{\circ}$), 6X-FFF, 15X from each -1.51, 0.83% and -0.63, -0.22% was not affected by the AP/PA direction represented. Setting the meter horizontally Gantry $90^{\circ}$, $270^{\circ}$ from the couch, Energy 6X-FFF 4.37, 2.84%, 15X, -9.63, -13.32% the difference. By-side direction measurements MapPHAN in value is not within the valid range can not, because that could be confirmed as gamma pass rate 3% of the value is greater than the value shown. You can check the Open Arc 6X-FFF, 15X energy, field size $10{\times}10$ cm $360^{\circ}$ rotation of the dose distribution in the state to look at nearly 90% pass rate to emerge. Conclusion: Based on the above results, the MapPHAN gantry direction dependence by side in the direction of the beam relative dose distribution suitable for measuring the gamma value, but accurate measurement of the absolute dose can not be considered is. this paper, a more accurate treatment plan in order to confirm, Reduce the tolerance for VMAT, such as lateral rotation investigation in order to measure accurate absolute isodose using a combination of IMF (Isocentric Mounting Fixture) MapCHEK 2, will be able to minimize the impact due to the angular dependence.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.73-92
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• 2014

An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.