• 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.

• The Korean Journal of Rheology
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• v.10 no.4
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• pp.234-246
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• 1998

The objective of this study is to investigate the correlation between steady shear flow (nonlinear behavior) and dynamic viscoelastic (linear behavior) properties for concentrated polymer solutions. Using both an Advanced Rheometic Expansion System(ARES) and a Rheometics Fluids Spectrometer (RFS II), the steady shear flow viscosity and the dynamic viscoelastic properties of concentrated poly(ethylene oxide)(PEO), polyisobutylene(PIB), and polyacrylamide(PAAm) solutions have been measured over a wide range of shear rates and angular frequencies. The validity of some previously proposed relationships was compared with experimentally measured data. In addition, the effect of solution concentration on the applicability of the Cox-Merz rule was examined by comparing the steady flow viscosity and the magnitude of the complex viscosity Finally, the applicability of the Cox-Merz rule was theoretically discussed by introducing a nonlinear strain measure. Main results obtained from this study can be summarized as follows : (1) Among the previously proposed relationships dealt with in this study, the Cox-Merz rule implying the equivalence between the steady flow viscosity and the magnitude of the complex viscosity has the best validity. (2) For polymer solutions with relatively lower concentration, the steady flow viscosity is higher than the complex viscosity. However, such a relation between the two viscosities is reversed for highly concentrated polymer solutions. (3) A nonlinear strain measure is decreased with increasing stran magnitude, after reaching the maximum value in small strain range. This behavior is different from the theoretical prediction demonstrating the shape of a damped oscillatory function. (4) The applicability of the Cox-Merz rule is influenced by the $\beta$ value, which indicates the slope of a nonlinear stain measure (namely, the degree of nonlinearity) at large shear deformations. The Cox-Merz rule shows better applicability as the $\beta$ value becomes smaller.

• Korean Journal of Soil Science and Fertilizer
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• v.26 no.2
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• pp.103-110
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• 1993

This study was made to evaluate the chemical properties of 481 farmers' orchard fields in An-Seong area. And the reasonable ferilizer rates were recommended utilizing the result of soil analysis. The results are summarized as follows : 1. As the soil fertility status of collected soil samples were evaluated on the basis of temporary optimal range for each soil chemical properties, 12.7 to 49.6% of the total 481 farmers' fields were range for soil improvement. 2. The contents of chemical component have a tendancy to decrease with depth gradually from surface to subsoil. 3. According to the relationship between the contents of soil component in subsoil and those in surface soil, the fertility condition of subsoil could be estimated on the basis of analysis data of surface soil. 4. The multiple regression equation for pear yield prediction to the organic matter and exchangable calcium contents in the soil were obtained. 5. Referring the average value, distribution ratio compared to the optimum level for each soil chemical properties and standard fertilizer rate, the soil fertility status could be categorized as "High" "Medium" and "Low". For each category, the recommended amounts for NPK and organic matter application were established. 6. The recommended rates through soul fertility diagnosis were less than farmer's dosage in the range 7.1~7.7 kg/10a for N, 0.8~11.5 kg/10a for $P_2O_5$, 7.1~19.9 kg/10a for $K_2O$ and 90~116 kg/10a for lime.

• Food Science of Animal Resources
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• v.28 no.3
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• pp.363-372
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• 2008

This study was conducted to investigate the relationship between the socio-demographic factors and the Korean consumers' palatability evaluation and to collect the sensory information for development of prediction palatability model of Hanwoo beef. Ten cuts [Abjin (short plate), Bosup (top sirloin), Cheggt (striploin), Dngsim (loin), Guri (chuck tender), Hongduke (eye of round), Moksim (chuck roll), Sulgit (bottom round), Udoon (top round), Yangji (brisket)] were separated from 5 Hanwoo steer beef (28-30 months old) and prepared with the same manner for 3 different cooking methods such as boiling, grilling, and roasting. The cooked beef samples were served to 650 consumers recruited from Seoul, Joongbu, Honam and Youngnam locations and evaluated as tenderness, juiciness, flavor, and overall acceptability. The consumer living location, age, gender, occupation, monthly income, and cut were significantly related with the scores of sensory properties when steer beef were cooked as boiling cooking (p<0.05). The eating habit and aging of beef were also significantly related with the tenderness and overall acceptability scores (p<0.01) when beef were cooked as boiled. In grill cooking, living location, aging, and cut had the significant effect on the sensory properties of Hanwoo steer beef (p<0.01). When steer beef were prepared by Korean traditional roast cooking, consumer's sensory scores were significantly different by the living location, age, occupation, monthly income, eating habit of consumers, aging and beef cut (p<0.001). Also, results from the principal component analysis showed that palatability scores of Korean consumers were decided with different contribution rate of tenderness, juiciness, flavor and overall acceptability for beef cut depending on cooking methods. In conclusion, Korean consumers' palatability for Hanwoo steer beef were related to the socio-demographic factors and the sensory scores were different by cut and cooking methods.

• Journal of Animal Science and Technology
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• v.49 no.6
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• pp.857-870
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• 2007

This study was conducted to investigate the relationship between the socio-demographic factors and the Korean consumers’ palatability evaluation and to collect the sensory information for development of prediction palatability model of Hanwoo beef. Ten cuts〔Abjin (short plate), Bosup (top sirloin), Cheggt (striploin), Dngsim (loin), Guri (chuck tender), Hongduke (eye of round), Moksim (chuck roll), Sulgit (bottom round), Udoon (top round), Yangi (brisket)〕 were separated from 10 Hanwoo bulls beef(～24 months old) and prepared with the same manner for 3 different cooking methods such as boiling, grilling and roasting. The cooked beef samples were served to 650 consumers recruited from Seoul, Joongbu, Honam and Youngnam locations and evaluated tenderness, juiciness, flavor and overall acceptability. The living location, age, gender, occupation, eating habit and cut were significantly related with the sensory properties when bull beef were cooked as boiled cooking(p<0.05). In grill cooking, only occupation, income and cut had the significant effect on the overall acceptability of Hanwoo bull beef(p<0.01). When bull beef were cooked as Korean traditional roast cooking, consumer’s sensory properties were significantly different by the living location, age, occupation, income, eating habit of consumers and cut(p<0.05). Also, results from the principal component analysis showed that palatability scores of Korean consumers were decided with different contribution scores of tenderness, juiciness, flavor and overall acceptability of cut depending on cooking methods. In conclusion, Korean consumers’ palatability for Hanwoo bull beef were related with the socio-demographic factors and the sensory results were different by cut and cooking methods.

• Journal of Animal Science and Technology
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• v.49 no.6
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• pp.871-880
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• 2007

This study was conducted to investigate the relationship between the socio-demographic factors and the Korean consumers’ palatability evaluation and to collect the sensory information for development of prediction palatability model of Hanwoo beef. Ten cuts〔Abjin (short plate), Bosup (top sirloin), Cheggt (striploin), Dngsim (loin), Guri (chuck tender), Hongduke (eye of round), Moksim (chuck roll), Sulgit (bottom round), Udoon (top round), Yangi (brisket)〕 were separated from 10 Hanwoo bulls beef(～24 months old) and prepared with the same manner for 3 different cooking methods such as boiling, grilling and roasting. The cooked beef samples were served to 650 consumers recruited from Seoul, Joongbu, Honam and Youngnam locations and evaluated tenderness, juiciness, flavor and overall acceptability. The living location, age, gender, occupation, eating habit and cut were significantly related with the sensory properties when bull beef were cooked as boiled cooking(p<0.05). In grill cooking, only occupation, income and cut had the significant effect on the overall acceptability of Hanwoo bull beef(p<0.01). When bull beef were cooked as Korean traditional roast cooking, consumer’s sensory properties were significantly different by the living location, age, occupation, income, eating habit of consumers and cut(p<0.05). Also, results from the principal component analysis showed that palatability scores of Korean consumers were decided with different contribution scores of tenderness, juiciness, flavor and overall acceptability of cut depending on cooking methods. In conclusion, Korean consumers’ palatability for Hanwoo bull beef were related with the socio-demographic factors and the sensory results were different by cut and cooking methods.

• Journal of Korean Society of Forest Science
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• v.89 no.3
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• pp.323-334
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• 2000

This study was carried out to investigate the effects of geological and topographical features on landslide and land-creep at the twenty four surveyed sites of Kyungpook province. According to the results obtained, it was concluded that continuous heavy rainfall was one of the primary factors to occur landslide and land-creep. Most of the landslides occurred in the past were concentrated in the granite and granitic gneiss zones, while land-creeps were mainly occurred in the mud-stone zones. Therefore, it was thought that the physical properties such as soil texture, solid phase, moisture contents, density, hardness and porosity rate of weathered granite and granitic gneiss could affect the occurrence of landslide and land-creep. Due to the holding of sand contents in the upper soil layers of weathered granite and granitic gneiss, rainfall could infiltrate into the soil easily. While lower soil layers contained much quantity of clay and silt contents, those soils saturated with rainfall cause to lose viscosity and shear strength. Therefore, it was seemed that landslide was occurred more easily and the saturation of those soils was made much easily by bed rocks under those soils. Landslide and land-creep are slided into lower place by gravitation and slope degree factors. Therefore, prediction of landslide occurrence is very difficult because landslide is occurred abruptly, and physical properties of the soil have to be understood and checking the existence of bed rocks under the soils is not easy, on the other hand, land-creep is progressed very slowly. Therefore, it was suggested that in a degree creeping could be protected by removing of several causing factors.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Korean Journal of Soil Science and Fertilizer
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• v.51 no.1
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• pp.16-27
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• 2018

To predict the influence of volcano eruption on agriculture in South Korea we evaluated the dispersal ranges of the volcanic ashes toward the South Korea based on the possibilities of volcano eruption in Mt. Baekdu. The possibilities of volcano eruption in Mt. Baekdu have been still being intensified by the signals including magmatic unrest of the volcano and the frequency of volcanic earthquakes swarm, the horizontal displacement and vertical uplift around the Mt. Baekdu, the temperature rises of hot springs, high ratios of $N_2/O_2$ and $_3He/_4He$ in volcanic gases. The dispersal direction and ranges and the predicted amount of volcanic ash can be significantly influenced by Volcanic Explosivity Index (VEI) and the trend of seasonal wind. The prediction of volcanic ash dispersion by the model showed that the ash cloud extended to Ulleung Island and Japan within 9 hours and 24 hours by the northwestern monsoon wind in winter while the ash cloud extended to northern side by the south-east monsoon wind during June and September. However, the ash cloud may extent to Seoul and southwest coast within 9 hours and 15 hours by northern wind in winter, leading to severe ash deposits over the whole area of South Korea, although the thickness of the ash deposits generally decreases exponentially with increasing distance from a volcano. In case of VEI 7, the ash deposits of Daejeon and Gangneung are $1.31{\times}10^4g\;m^{-2}$ and $1.80{\times}10^5g\;m^{-2}$, respectively. In addition, ash particles may compact close together after they fall to the ground, resulting in increase of the bulk density that can alter the soil physical and chemical properties detrimental to agricultural practices and crop growth.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1031-1031
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• 2001

The mammary gland is made up of remarkably sensitive tissue, which has the capability of producing a large volume of secretion, milk, under normal or healthy conditions. When bacteria enter the gland and establish an infection (mastitis), inflammation is initiated accompanied by an influx of white cells from the blood stream, by altered secretory function, and changes in the volume and composition of secretion. Cell numbers in milk are closely associated with inflammation and udder health. These somatic cell counts (SCC) are accepted as the international standard measurement of milk quality in dairy and for mastitis diagnosis. NIR Spectra of unhomogenized composite milk samples from 14 cows (healthy and mastitic), 7days after parturition and during the next 30 days of lactation were measured. Different multivariate analysis techniques were used to diagnose the disease at very early stage and determine how the spectral properties of milk vary with its composition and animal health. PLS model for prediction of somatic cell count (SCC) based on NIR milk spectra was made. The best accuracy of determination for the 1100-2500nm range was found using smoothed absorbance data and 10 PLS factors. The standard error of prediction for independent validation set of samples was 0.382, correlation coefficient 0.854 and the variation coefficient 7.63%. It has been found that SCC determination by NIR milk spectra was indirect and based on the related changes in milk composition. From the spectral changes, we learned that when mastitis occurred, the most significant factors that simultaneously influenced milk spectra were alteration of milk proteins and changes in ionic concentration of milk. It was consistent with the results we obtained further when applied 2DCOS. Two-dimensional correlation analysis of NIR milk spectra was done to assess the changes in milk composition, which occur when somatic cell count (SCC) levels vary. The synchronous correlation map revealed that when SCC increases, protein levels increase while water and lactose levels decrease. Results from the analysis of the asynchronous plot indicated that changes in water and fat absorptions occur before other milk components. In addition, the technique was used to assess the changes in milk during a period when SCC levels do not vary appreciably. Results indicated that milk components are in equilibrium and no appreciable change in a given component was seen with respect to another. This was found in both healthy and mastitic animals. However, milk components were found to vary with SCC content regardless of the range considered. This important finding demonstrates that 2-D correlation analysis may be used to track even subtle changes in milk composition in individual cows. To find out the right threshold for SCC when used for mastitis diagnosis at cow level, classification of milk samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two levels of SCC - 200 000 cells/$m\ell$ and 300 000 cells/$m\ell$, respectively, were set up and compared as thresholds to discriminate between healthy and mastitic cows. The best detection accuracy was found with 200 000 cells/$m\ell$ as threshold for mastitis and smoothed absorbance data: - 98% of the milk samples in the calibration set and 87% of the samples in the independent test set were correctly classified. When the spectral information was studied it was found that the successful mastitis diagnosis was based on reviling the spectral changes related to the corresponding changes in milk composition. NIRS combined with different ways of spectral data ruining can provide faster and nondestructive alternative to current methods for mastitis diagnosis and a new inside into disease understanding at molecular level.