• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• 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.6
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• pp.1306-1313
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• 2011

A liquid fertilizer treated with slurry composting and biofiltration (SCB) process has been applied increasingly on agricultural field but the effects on the soil properties and crop production has not been throughly evaluated. This study was conducted to investigate the effect of the SCB application on soil chemical properties and the growth of radish and corn. SCB liquid fertilizer as a basal fertilization was treated with five levels based on $6kg\;10a^{-1}$ for radish and $10kg\;10a^{-1}$ for corn. The experimental design was the completely randomized block design with five levels and three replicates. Electrical conductivity (EC), $NO_3$-N, Exch. K and Exch. Na increased depending on the treatment levels of SCB. There were no changes in soil organic matter, Avail. $P_2O_5$, Exch. Ca and Exch. Mg. EC, $NO_3$-N and Exch. Na content decreased as precipitation increased. Especially, they decreased up to the initial condition before the treatment after the heavy rainy season in 2008. Although Exch. K decreased at the rainy season, they remained relatively higher content after the experiment on August, 2008. Fresh weight and the amount of N uptake of radish increased due to the levels of SCB, but corn did not present any significant increase. It is recommended that we need to decide the proper amount of SCB as well as the application method on the field to increase the productivity and decrease environmental stress. Additional experiments also need to clarify the effect of the trace element and heavy metal accumulations due to long term application of SCB.

• 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.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.1
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• pp.57-70
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• 2009

Considerable advance has been made on research on effect of steel pipe Umbrella Arch Method (UAM) and mechanical reinforcement mechanism through numerical analyses and experiments. Due to long analysis time of three-dimensional analysis and its complexity, un-quantitative two-dimensional analysis is dominantly used in the design and application, where equivalent material properties of UAM reinforced area and ground are used, For this reason, development of reasonable, theoretical, quantitative and easy to use design and analysis method is required. In this study, both field UAM tests and laboratory tests were performed in the residual soil to highly weathered rock; field tests to observe the range of reinforcement, and laboratory tests to investigate the change of material properties between prior to and after UAM reinforcement. It has been observed that the increase in material property of neighboring ground is negligible, and that only stiffness of steel pipe and cement column formed inside the steel pipe and the gap between steel pipe and borehole contributes to ground reinforcement. Based on these results and concept of Convergence Confinement Method (CCM), two dimensional axisymmetric analyses have been performed to obtain the longitudinal displacement profile (LDP) corresponding to arching effect of tunnel face, UAM effect and effect of supports. In addition, modified load distribution method in two dimensional plane-strain analysis has been suggested, in which effect of UAM is transformed to internal pressure and modified load distribution ratios are suggested. Comparison between the modified method and conventional method shows that larger displacement occur in the conventional method than that in the modified method although it may be different depending on ground condition, depth and size of tunnel, types of steel pipe and initial stress state. Consequently, it can be concluded that the effect of UAM as a beam in a longitudinal direction is not considered properly in the conventional method.

• Korean Journal of Agricultural Science
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• v.24 no.2
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• pp.145-155
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• 1997

The large scaled field test by prefabricated vertical drains was performed to evaluate the superiority of vertical discharge capacity for drain materials through compare and analyze the time-settlement behavior with drain spacing and the compression index and consolidation coefficient obtained by laboratory experiments and field monitoring system. 1. The relation of measurement settlement($S_m$) versus design settlement($S_t$) and measurement consolidation ratio($U_m$) versus design consolidation ratio($U_t$) were shown $S_m=(1.0{\sim}1.1)S_t$, $U_m=(1.13{\sim}1.17)U_t$ at 1.0m drain spacing and $S_m=(0.7{\sim}0.8)S_t$, $U_m=(0.92{\sim}0.99)U_t$ at l.5m drain spacing, respectively. 2. The relation of field compressing index($C_{cfield}$) and virgin compression index($V_{cclab.}$) was shown $C_{cfield}=(1.0{\sim}1.2)V_{cclab.}$, But it was nearly same value when considered the error with determination method of virgin compression index and prediction method of total settlement. 3. Field consolidation coefficient was larger than laboratory consolidation coefficient, and the consolidation coefficient ratio($C_h/C_v$) were $C_h=(2.4{\sim}3.0)C_v$. $C_h=(3.5{\sim}4.3)C_v$ at 1.0m and 1.5m drain spacing and increased with increasing of drain spacing. 4. The evaluation of vertical discharge capacity with drain spacing from the results of the consolidation coefficient ratio showed largely superior in case the Mebra drain and Amer drain than other drain materials at 1.0m and 1.5m drain spacing, while the values showed nearly same value in case same drain spacing.

• Journal of The Korean Society of Grassland and Forage Science
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• v.32 no.1
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• pp.15-28
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• 2012

This study was carried out to know adaptability and growth characteristics, yield, chemical compositions and nutrition yield of corn hybrids for silage at paddy field. The field experiments were conducted at Sangju province for one year (2009). The experimental design was arranged in a randomized block design with three replication. The treatments consisted of eleven corn hybrids. The planting date was on 1 May and harvested at 24 August. Stem diameter, stem hardness and number of ear were higher in P32P75 than other varieties. Ear height, dead leaf and green degree were highest in $NC^+$7117, but number of root system and Brix ($B^{\circ}$) were higher in P3394 than other varieties. Crude protein and crude fat (EE) were highest in P32K61 and P31P41, respectively (P<0.01). NDF and ADF were highest in KPO and KIO, respectively, but no significant differences were found among the varieties. Total mineral contents were the highest in Kwangpyongok (9,775 mg/kg), and P3394 (6,651 mg/kg) was the lowest as compared to other varieties (P<0.01). Crude protein yield, crude fat yield and mineral yield were highest in P3156, P31P41 and KPO, respectively (P<0.01). Total composition amino acid and total fatty acid were the highest in P32K61 and KIO, respectively (P<0.01). Yields of crude protein, fatty acid, composition amino acid and TDN were the highest in P3156 (P<0.01). But yields of crude protein and mineral were the highest in P31P41 and KPO, respectively (P<0.01). Total digestible nutrient (TDN) was higher in order of P3156 > $NC^+$7117 > P31N27 > KPO > P32K61 > P32T83 > P32P75 > P31P41 > P3394 > P32W86 > KIO. Based on the above results, corn hybrid varieties could be recommended in P3156, NC+7117 and P31N27 for growth characteristics, quantitative production and nutrition yield.

• Science of Emotion and Sensibility
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• v.19 no.3
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• pp.103-120
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• 2016

This study investigated how video playback styles affect viewers' emotional responses to a video and then suggested emotion assessment tool for playback-edited videos. The study involved two in-lab experiments. In the first experiment, observers were asked to express their feelings while watching videos in both original playback and articulated playback simultaneously. By controlling the speed, direction, and continuity, total of twelve playback styles were created. Each of the twelve playback styles were applied to five kinds of original videos that contains happy, anger, sad, relaxed, and neutral emotion. Thirty college students participated and more than 3,800 words were collected. The collected words were comprised of 899 kinds of emotion terms, and these emotion terms were classified into 52 emotion categories. The second experiment was conducted to develop proper emotion assessment tool for playback-edited video. Total of 38 emotion terms, which were extracted from 899 emotion terms, were employed from the first experiment and used as a scales (given in Korean and scored on a 5-point Likert scale) to assess the affective quality of pre-made video materials. The total of eleven pre-made commercial videos which applied different playback styles were collected. The videos were transformed to initial (un-edited) condition, and participants were evaluated pre-made videos by comparing initial condition videos simultaneously. Thirty college students evaluated playback-edited video in the second study. Based on the judgements, four factors were extracted through the factor analysis, and they were labelled "Happy", "Sad", "Reflective" and "Weird (funny and at the same time weird)." Differently from conventional emotion framework, the positivity and negativity of the valence dimension were independently treated, while the arousal aspect was marginally recognized. With four factors from the second experiment, finally emotion assessment tool for playback-edited video was proposed. The practical value and application of emotion assessment tool were also discussed.

• KSBB Journal
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• v.25 no.2
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• pp.142-154
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• 2010

The protein-bound innerpolysaccharides (IPS) produced by suspended mycelial cultures of Inonotus obliquus have promising potentials as an effective antidiabetic as well as an immunostimulating agents. To enhance IPS production, intensive strain improvement process should be carried out using large amount of UV-mutated protoplasts. During the whole strain-screening process, the stage of solid growth-culture was found to be the most time-requiring step, thus preventing rapid screening of high-yielding producers. In order to reduce the cell growth period in the solid growth-stage, therefore, solid growth-medium was optimized using the statistical methods such as (i) Plackett-Burman and fractional factorial designs (FFD) for selecting positive medium components, and (ii) steepest ascent (SAM) and response surface (RSM) methods for determining optimum concentrations of the selected components. By adopting the medium composition recommended by the SAM experiment, significantly higher growth rate was obtained in the solid growth-cultures, as represented by about 41% larger diameter of the cell growth circle and higher mycelial density. Sequential optimization process performed using the RSM experiments finally recommended the medium composition as follows: glucose 25.61g/L, brown rice 12.53 g/L, soytone peptone 12.53 g/L, $MgSO_4$ 5.53 g/L, and agar 20 g/L. It should be noted that this composition was almost similar to the medium combinations determined by the SAM experiment, demonstrating that the SAM was very helpful in finding out the final optimum concentrations. Through the use of this optimized medium, the period for the solid growth-culture could be successfully reduced to about 8 days from the previous 15~20 days, thus enabling large and mass screening of high producers in a relatively short period.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.485-496
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• 2022

Modern agriculture is being transformed into smart agriculture to maximize production efficiency along with changes in the 4th industrial revolution. However, rural areas in Korea are facing challenges of aging, low fertility, and population outflow, making it difficult to transition to smart agriculture. Among ICT technologies, simulation allows users to observe or experience the results of their choices through imitation or reproduction of reality. The combination of the three-dimension (3D) model and the greenhouse simulator enable a 3D experience by virtual greenhouse for fruits and vegetable cultivation. At the same time, it is possible to visualize the greenhouse under various cultivation or climate conditions. The objective of this study is to apply the greenhouse climate management model for simulation development that can visually see the state of the greenhouse environment under various micrometeorological properties. The numerical solution with the mathematical model provided a dynamic change in the greenhouse environment for a particular greenhouse design. Light intensity, crop transpiration, heating load, ventilation rate, the optimal amount of CO₂ enrichment, and daily light integral were calculated with the simulation. The results of this study are being built so that users can be linked through a web page, and software will be designed to reflect the characteristics of cladding materials and greenhouses, cultivation types, and the condition of environmental control facilities for customized environmental control. In addition, environmental information obtained from external meteorological data, as well as recommended standards and set points for each growth stage based on experiments and research, will be provided as optimal environmental factors. This simulation can help growers, students, and researchers to understand the ICT technologies and the changes in the greenhouse microclimate according to the growing conditions.