• Journal of the Korea Computer Graphics Society
• /
• v.22 no.5
• /
• pp.17-26
• /
• 2016

This paper presents an adaptive slicing method based on merging vertical layer polylines. Firstly, we slice the input 3D polygon model uniformly with the minimum printable thickness, which results in bounding polylines of the cross section at each layer. Next, we group a set of layer polylines according to vertical connectivity. We then remove polylines in overdense area of each group. The number of layers to merge is determined by the layer thickness computed using the cusp height of the layer. A set of layer polylines are merged into a single polyline by removing the polylines within the layer thickness. The proposed method maintains the shape features as well as reduces the printing time. For evaluation, we sliced ten 3D polygon models using our method and a global adaptive slicing method and measured the total length of polylines which determines the printing time. The result showed that the total length from our method was shorter than the other method for all ten models, which meant that our method achieved less printing time.

• Journal of Pharmaceutical Investigation
• /
• v.29 no.3
• /
• pp.193-203
• /
• 1999

In order to investigate systematically the steady shear flow properties of aqueous po1y(ethylene oxide) (PEO) solutions having various molecular weights and concentrations, the steady flow viscosity has been measured with a Rheometrics Fluids Spectrometer (RFS II) over a wide range of shear rates. The effects of shear rate, concentration, and molecular weight on the steady shear flow properties were reported in detail from the experimentally measured data, and then the results were interpreted using the concept of a material characteristic time. In addition, some flow models describing the non-Newtonian behavior (shear-thinning characteristics) of polymeric liquids were employed to make a quantitative evaluation of the steady flow behavior, and the applicability of these models was examined by calculating the various material parameters. Main results obtained from this study can be summarized as follows: (1) At low shear rates, aqueous PEO solutions show a Newtonian viscous behavior which is independent of shear rate. At shear rate region higher than a critical shear rate, however, they exhibit a shear-thinning behavior, demonstrating a decrease in steady flow viscosity with increasing shear rate. (2) As an increase in concentration and/or molecular weight, the zero-shear viscosity is increased while the Newtonian viscous region becomes narrower. Moreover, the critical shear rate at which the transition from the Newtonian to shear-thinning behavior occurs is decreased, and the shear-thinning nature becomes more remarkable. (3) Aqueous PEO solutions show a Newtonian viscous behavior at shear rate range lower than the inverse value of a characteristic time $1/{\lambda}_E$, while they exhibit a shear-thinning behavior at shear rate range higher than $1/{\lambda}_E$. For aqueous PEO solutions having a broad molecular weight distribution, the inverse value of a characteristic time is not quantitatively equivalent to the critical shear rate, but the power-law relationship holds between the two quantities. (4) The Cross, Carreau, and Carreau-Yasuda models are all applicable to describe the steady flow behavior of aqueous PEO solutions. Among these models, the Carreau-Yasuda model has the best validity.

• Journal of the Korean Society of Earth Science Education
• /
• v.10 no.1
• /
• pp.76-89
• /
• 2017

The purpose of this study is to develop a science field study area using Geumgang(Geum River), fossil origins and various geological resources in Youngdong area of Chungcheongbuk-do as educational resources; and utilize them to develop an education program to cultivate earth science and topophilia. The Youngdong sedimentary basin (Cretaceous period) has a well-developed outcrop along the Geumgang and it is therefore easy to find various geological structures, plant fossils, and dinosaur fossils. Also, it has a distinct sedimentary structure, such as mud cracks, ripple marks and cross-bedding. Science field study area(6 observation sites) were developed based on school curriculum, textbook analysis, and professional earth science education panel discussion to create a convergence education program. The result of validating the developed program showed that all the items were satisfactory ($CVR{\geq}0.88$) in the test categories. The science field study teaching-learning model was applied to actual classes. The evaluation result for class satisfaction was positive, scoring Rickert scale 4.18. The result of observation about the outdoor classroom process in the science field study area revealed that students were able to form a new image of the beautiful scenery of the Geumgang. Also, the students could gain a new understanding, concept and value of various geological objects (sandy beach, stepping-stones, dinosaur footprint fossils, sedimentary formation), which naturally allowed them to form topophilia.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.2
• /
• pp.1126-1139
• /
• 2014

World is experiencing abnormal weather caused by urbanization and industrialization increasing greenhouse gas and one of these phenomenon domestically happening is flood and drought. The increase of green-house gases is due to urbanization and industrialization acceleration which are causing abnormal climate changes such as the El Nino and a La Nina phenomenon. It is expected that there will be many difficulties in water management, especially considering the topography and seasonal circumstances in Korea. Unlike in the past, a variety of water conservation initiatives have been undertaken like the river-management flow and water capacity expansion projects. To meet the increasing demand for water resources, new environmentally-friendly small and medium-sized dams have been built. Therefore, the development of a new paradigm for water resources management is essential. This study shows that additional security is needed for potential water resources through diversion tunnels and is very important to consider for future water supplies and situations. Using RCP 6.0 and RCP 8.5 in representative concentration pathway climate change scenario, specific hydrologic data of study basin was produced to analyze past observed basin rainfall tendency which showed both scenario 5%~9% range increase in rainfall. Through sensitivity analysis using objective function, population in highest goodness was 1000 and cross rate was 80%. In conclusion, it is expected that the results from this study will help to make long-term and stable water supply plans by using the potential water resource evaluation model which was applied in this study.

• The Journal of Korean Academy of Prosthodontics
• /
• v.61 no.1
• /
• pp.33-43
• /
• 2023

Pathological wear across the entire dentition causes problems such as collapsed occlusal plane, reduced vertical dimension, anterior premature contact, inadequate anterior guidance, and tooth migration, thereby induce symptoms such as temporomandibular joint disorder, reduced masticatory efficiency, and tooth hypersensitivity. For the treatment of patients with excessive wear, evaluation of vertical dimension should be preceded along with analysis of the cause. The patient in this case was a 45-year-old female with a history of orthognathic surgery. Through clinical examination, radiographic examination, and model analysis, overall tooth wear, interdental spacing in the anterior maxillary region, retruded condylar position, and insufficient interocclusal space for prosthetic restoration were confirmed. Full mouth rehabilitation with increased vertical dimension was planned, the patient's adaptation to the new vertical dimension was evaluated with a removable occlusal splint and temporary prosthesis, and cross-mounting was performed based on the temporary restoration to fabricate the definitive zirconia prosthesis, maintaining the adjusted vertical dimension. It showed satisfactory functional and esthetic results through stable restoration of the occlusal relationship.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.163-177
• /
• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Life Science
• /
• v.16 no.6
• /
• pp.904-910
• /
• 2006

Dendritic cells (DCs) are the only antigen presenting cells (APCs) capable of initiating immune responses, which is crucial for priming the specific cytotoxic T lymphocyte (CTL) response and tumor immunity. Upon activation by DCs, CD4+ helper T cells can cross-prime CD8+ CTLs via IL-12. However, recently activated DCs were described to prime in vitro strong T helper cell type 1 $(Th_1)$ responses, whereas at later time points, they preferentially prime $Th_2$ cells. Therfore, we examined in this study the optimum kinetic state of DCs activation impacted on in vivo priming of tumor-specific CTLs by using ovalbumin (OVA) tumor antigen model. Bone-marrow-derived DCs showed an appropriate expression of surface MHC and costimulatory molecules after 6 or 7-day differentiation. The 6-day differentiated DCs pulsed with OVA antigen for 8 h (8-h DC) and followed by restimulation with LPS for 24 h maintained high interleukin (IL)-12 production potential, accompanying the decreased level in their secretion by delayed re-exposure time to LPS. Furthermore, immunization with 8-h DC induced higher intracellular $interferon(IFN)-{\gamma}+/CD8+T$ cells and elicited more powerful cytotoxicity of splenocytes to EG7 cells, a clone of EL4 cells transfected with an OVA cDNA, than immunization with 24-h DC. In the animal study for the evaluation of therapeutic or protective antitumor immunity, immunization with 8-h DC induced an effective antitumor immunity against tumor of EG7 cells and completely protected mice from tumor formation and prolonged survival, respectively. The most commonly used and clinically applied DC-based vaccine is based on in vitro antigen loading for 24 h. However, our data indicated that antigen stimulation over 8 h decreased antitumor immunity with functional exhaustion of DCs, and that the 8-h DC would be an optimum activation state impacted on in vivo priming of tumor-specific CTLs and subsequently lead to induction of strong antitumor immunity.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.39 no.2
• /
• pp.114-120
• /
• 2019

This study was carried out to explore the accuracy of near infrared spectroscopy(NIRS) for the prediction of moisture content and chemical parameters on winter annual forage crops. A population of 2454 winter annual forages representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation($R^2$) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS calibration model to predict the moisture contents and chemical parameters had very high degree of accuracy except for barely. The $R^2$ and SECV for integrated winter annual forages calibration were 0.99(SECV 1.59%) for moisture, 0.89(SECV 1.15%) for acid detergent fiber, 0.86(SECV 1.43%) for neutral detergent fiber, 0.93(SECV 0.61%) for crude protein, 0.90(SECV 0.45%) for crude ash, and 0.82(SECV 3.76%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of winter annual forage for routine analysis method to evaluate the feed value.

• Analytical Science and Technology
• /
• v.28 no.6
• /
• pp.436-445
• /
• 2015

Amomi Fructus with anti-oxidative activity was chosen and essential oil was obtained by SDE (simultaneous distillation extraction), and 39 constituents were determined by GC-MS (gas chromatography-mass spectrometry). Major components were camphor, borneol acetate, borneol, D-limonene and camphene. Three solvent extracts such as hexanes, diethyl ether and methylene chloride from Amomi Fructus were obtained. These were analyzed by GC-MS and 4 more constituents were identified in addition to 39 components discovered in essential oil. Five major components such as camphor, borneol acetate, borneol, D-limonene and camphene were also detected, however the relative peak percents of those components were different from those of constituents in essential oil. To estimate the kind and the amount of materials evaporated at certain temperature and conditions from essential oil and solvent extracts, dynamic headspace apparatus was used and materials evaporated and trapped at certain conditions were analyzed by GC-MS. Recovery yield of SDE method from Amomi Fructus was measured by using camphor and standard calibration solution of camphor methanol solution and, the yield was 82.0%. Content of Hg was measured by mercury analyzer and contents of Cd, Pb, Cr, Mn, Co, Ni, Cu and Zn in Amomi Fructus, essential oils and solvent extracts were determined by ICP-MS (Inductively coupled plasma-mass spectrometer). Pb, Cd and Hg were measured in the concentration of 0.72 mg/kg, <0.10 mg/kg and 0.0023 mg/kg, respectively and these were below permission level of purity test. Contents of Mn, Cu and Zn in Amomi Fructus were 213 mg/kg, 8.29 mg/kg and 31.0 mg/kg, respectively and which were relatively higher than other metals such as Cr, Co and Ni. Metals such as Mn (0.65 ~ 9.08 mg/kg), Cu (1.16 ~ 4.40 mg/kg) and Zn (1.10 ~ 3.80 mg/kg) in essential oil and solvent extracts were detected. At this point it is not clear that the metals were cross-contaminated in the course of treating Amomi Fructus or metals were contained in Amomi Fructus. The influence evaluation toward biological model study of these metals in essential oil and solvent extracts will be needed.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.23-45
• /
• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.