• Anatomy & Biological Anthropology
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• v.31 no.4
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• pp.133-142
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• 2018

3D histology is a imaging system for the 3D structural information of cells or tissues. The synchrotron radiation propagation phase contrast micro-CT has been used in 3D imaging methods. However, the simple phase contrast micro-CT did not give sufficient micro-structural information when the specimen contains soft elements, as is the case with many biomedical tissue samples. The purpose of this study is to develop a new technique to enhance the phase contrast effect for soft tissue imaging. Experiments were performed at the imaging beam lines of Pohang Accelerator Laboratory (PAL). The biomedical tissue samples under frozen state was mounted on a computer-controlled precision stage and rotated in $0.18^{\circ}$ increments through $180^{\circ}$. An X-ray shadow of a specimen was converted into a visual image on the surface of a CdWO4 scintillator that was magnified using a microscopic objective lens(X5 or X20) before being captured with a digital CCD camera. 3-dimensional volume images of the specimen were obtained by applying a filtered back-projection algorithm to the projection images using a software package OCTOPUS. Surface reconstruction and volume segmentation and rendering were performed were performed using Amira software. In this study, We found that synchrotron phase contrast imaging of frozen tissue samples has higher contrast power for soft tissue than that of non-frozen samples. In conclusion, synchrotron radiation propagation phase contrast cryo-microCT imaging offers a promising tool for non-destructive high resolution 3D histology.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.2
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• pp.161-167
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• 2011

The soil moisture has an important effect on growth and development of highbush blueberry (HB), mainly because the root system, devoid of root hairs, is superficial. Moreover, the texture and organic matter content of Korean soil is different from the main producing counties, such as USA and Canada. To facilitate the growth and development of HB and long-term maintenance of productivity, the research related to soil moisture condition in Korea should be the priority. This study was performed to investigate the growth properties of the HB in various soil moisture conditions in order to determine the irrigation trigger point and optimum soil water potential. The texture of soil used in this experiment was loam. For the experiments, the soil was mixed with peatmoss at a rates 30% (v/v). Irrigation was scheduled at -3, -4, -5, -8, -15 and -22 kPa soil water potential then investigated leaf macronutrient, bush growth, and fruit properties. The leaf K content of HB showed the same trend in the soil water potential, but Leaf P and Mg content was highest in -5 and -22 kPa, respectively. The productivity and growth amount of HB showed the peak at the range of -4~-8 kPa as normal distribution pattern, and greatly decreased at above -15 kPa. Total dry weight and Cane diameter were highest at -4 kPa, plant width, fruit weight and yield were highest at -5 kPa, and plant height, cane number and shoot tension were highest at -8 kPa. Soluble solids content showed same trend in the soil water potential, but titratable acidity, anthocyanins and total polyphenols were not significantly different. Therefore, the optimal soil water potential for the development and a maximum production of HB were a range of -4~-8 kPa, and the recommended ideal irrigation trigger point was within -15 kPa.

• Journal of The Korean Society of Grassland and Forage Science
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• v.31 no.1
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• pp.55-64
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• 2011

This study was conducted to investigate the effects of green manure crops and cattle slurry application on rice productivity and nutrient movement in paddy land. Cropping systems used in this study were consisted of five designs, such as mono-cropping rice applied with standard fertilizer (MRS), double-cropping whole crop barley following rice applied with cattle slurry (DWBRC), double-cropping whole crop rye following rice applied with cattle slurry (DWRRC), mono-cropping rice applied with following milk vetch (MRMV) and mono-cropping rice applied with following hairy vetch (MRHV). The field experiments were conducted on the clay loam at Backsanmyun, Kimje, Chunlabukdo province in Korea for three years (May 2006 to Apr. 2009). This study was arranged in completely randomized design with three replicates. Culm and panicle length of rice were higher in MRS than other treatments. Ripened grain ratio was higher in MRS than other treatments. Yield of Grain showed high in MRS, DWBRC and DWRRC than MRMV and MRHV. The yield of dry matter (DM) of whole crop barley in DWBRC increased significantly as compared with that of rye in DWRRC. The pH, and contents of T-N, $P_2O_5$ and organic matter (OM) and exchangeable cation (Ca, Na, Mg and K) in soil samples collected at the end of the experiment were remarkably higher than those at the beginning of the experiment. The concentrations of $NO_3$-N, $NH_4$-N and $PO_4$ in discharge water in DWBRC and DWRRC were higher than MRS.

• Korean Journal of Ecology and Environment
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• v.44 no.1
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• pp.75-84
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• 2011

We examined the effect of the turbid water on the periphytic diatom community in an artificial stream system. The artificial stream was constructed with transparent acryl and composed of four channels. Each channel ($20\;cm{\times}200\;cm{\times}40\;cm$) was supplied continuously with eutrophic lake water. In order to the freely colonize and grow diatoms, artificial substrate was installed with commercial slide glass soaked in 1% agar. Prior to introducing turbid water, the artificial stream was operated with lake water for 6 days to permit the propagation of diatom community on the substrates. The turbid water prepared with sediment sieved with ${\varphi}$ $64\;{\mu}m$ at $2\;g\;L^{-1}$ (final concentration, 300 NTU) was provided daily for 50 minute duration. The experiment was conducted for 7 days with manipulated experimental condition of light ($50{\sim}80\;{\mu}mol\;m^{-2}s^{-1}$, light:dark=24:0), temperature ($10{\pm}1^{\circ}C$), and flow rate ($0.31\;cm\;s^{-1}$). Sampling and analysis were conducted daily for water quality and diatom. Turbidity of the water varied 162.2~173.2 NTU during the experiment. After introduction of turbid water, DO, pH and TN were decreased, while SS and TP increased significantly. A total of 14 genera and 47 species of diatoms was observed on the artificial substrates during the experimental period. Of these, Navicula appeared to be a most dominant genus with 10 species, followed by Cymbella (6 species), Fragilaria (6 species) and Gomphonema (5 species). Achnanthes minutissima was the most dominant species (>70% of total frequency) in both control and treatment experiments. Increase in diatom abundance lasted for three days since turbid water introduction, after that they gradually decreased by the termination of the experiment. These results suggest that frequent supply of highly-concentrated turbid water significantly decreases the periphytic diatom community, and retard the recovery of the stable food-web within the stream.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.226-234
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• 2020

As the average life expectancy of human beings is extended in addition to the entry of the aging society, there is a tendency for the interest in the appearance of men and women in modern society to increase. The most external judgment of human aging is the wrinkles on the facial skin. People are undergoing various procedures to have clean, wrinkled, and resilient healthy skin. Many thread lifting procedures are being implemented because they tend to want simple and effective procedures during the procedure. In this study, in order to improve lifting effect in thread lifting, animal experiments were conducted to confirm the improvement of wrinkles by injecting RF high frequency and CO₂ gas into existing PDO suture procedures. The experimental groups consisted of natural aging groups, PDO treatment groups, groups with RF high frequency in PDO procedures, groups with CO₂ gas injected into PDO procedures, and groups with CO₂ gas and RF injected simultaneously into PDO procedures. The individuals in the natural aging group had an average wrinkle depth of 0.408mm before the procedure, and the average wrinkle depth of the 10th week was 0.68mm. The depth of wrinkles in the PDO treatment group averaged 0.384mm before the procedure, and 0.348mm on the 10th week after the procedure. The average crease depth of pre-procedure objects injected with RF high frequency in PDO was 0.42mm, and the average crease depth for 10 weeks was 0.378mm. The average crease depth of the CO₂ gas injected into the PDO was 0.4mm before the procedure, and the average crease depth was reduced to 0.332mm in the 10th week after the procedure. On average, the number of objects injected with CO₂ gas and RF high frequency in the PDO procedure decreased to 0.412mm before and 0.338mm in the 10th week after the procedure. The procedure of injecting CO₂ gas and RF into the PDO suture showed the highest reduction rate of 17.96%.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.