• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.301-306
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• 2018

Urbanization causes urban floods and urban heat island in the summer, so it is necessary to understanding the changes of the thermal environment through urban climate and energy balance. This can be explained by the energy balance, but in urban areas, unlike the typical energy balance, the storage heat flux saved in the building or artificial land cover should be considered. Since the environment of each city is different, there is a difficulty in applying the method of retrieving the storage heat flux of the previous research. Especially, most of the previous studies are focused on the overseas cities, so it is necessary to study the storage heat retrieval suitable for various land cover and building characteristics of the urban areas in Korea. Therefore, the object of this study, it is to derive the regression formula which can quantitatively retrieve the storage heat using the data of the area where various surface types exist. To this end, nonlinear regression analysis was performed using net radiation and surface temperature data as independent variables and flux tower based storage heat estimates as dependent variables. The retrieved regression coefficients were applied to each independent variable to derive the storage heat retrieval regression formula. As a result of time series analysis with flux tower based storage heat estimates, it was well simulated high peak at day time and the value at night. Moreover storage heat retrieved in this study was possible continuous retrieval than flux tower based storage heat estimates. As a result of scatter plot analysis, accuracy of retrieved storage heat was found to be significant at $50.14Wm^{-2}$ and bias $-0.94Wm^{-2}$.

• The Journal of Korean Academy of Prosthodontics
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• v.50 no.4
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• pp.292-298
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• 2012

Purpose: The purpose of this study is to ascertain the stability of the implant by comparing the effects of the change of implant diameter, length and design on implant stability quotient. Materials and methods: To remove the variable due to the difference of bone quality, the uniform density (0.48 g/$cm^3$) Polyuretane foam blocks (Sawbones$^{(R)}$, Pacific Research Laboratories Inc, Vashon, Washington) were used. Implants (Implantium$^{(R)}$, Dentium, Seoul, Korea) were placed with varying diameters (${\phi}3.8$, ${\phi}4.3$ and ${\phi}4.8$) and length (8 mm, 10 mm and 12 mm), to assess the effect on implant stability index (ISQ). Also the influence of the design of the submerged and the non-submerged (SimplelineII$^{(R)}$, Dentium, Seoul, Korea) on ISQ was evaluated. To exclude the influence of insertion torque, a total of 60 implants (n = 10) were placed with same torque to 35 N. Using Osstell$^{TM}$ mentor (Integration Diagnostic AB, Sweden) ISQ values were recorded after measuring the resonant frequency, one-way ANOVA and Tukey HSD test results were analyzed. (${\alpha}$=0.05). Results: 1. The change of the diameter of the implant did not affect the ISQ (P>.05), but the increase of implant length increased the ISQ(P<.001). 2. The change in implant design were correlated with the ISQ, and the ISQ of submerged design was significantly higher than that of the non-submerged design(P<.05). Conclusion: In order to increase implant stability, the longer implant is better to be selected, and on the same length of implant, submerged design is thought to be able to get a higher ISQ than the non-submerged.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.604-617
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• 2018

As the depletion of forests became more widespread due to the increase in the number of roads, the research was conducted on the relationship between temperature and humidity in the forests, assuming that the forests around the roads were affected. Through the forest monitoring, the temperature and humidity of coniferous forests and broadleaf forests in Sedong and Gongju areas were observed at three point of 10m, 20m and 30m from the road boundary to the inside of the forest, respectively. In Yeongdong area, for more reliable results, it was observed from the point of 0m, 10m, and 20m. During the study period, so it was expected the change in tree growth was small, the change of temperature and humidity inside the forest by the road was compared with the temperature and humidity from the road to the inside of the forest from September 2017 to January 2018, the changes of temperature and humidity inside the forest due to linear development such as roads were quantitatively analyzed. Using the HOBO data logger (MX2301, Onset Corp.), the temperature and humidity changes of each site were measured, and the average of the changes have been analyzed monthly. In the case of Gongju coniferous forests in September 2017, the average weekly temperature is $0.57^{\circ}C$ higher than the forest outside from the forest boundary and $1.23^{\circ}C$ higher than the inside of the forest, at night in November 2017, in Sedong broadleaf forests. That is, the ability to control the temperature and humidity of the forests along the road was larger and less variable as the distance from the road boundary to the inside of the forest increased. In this study, it is considered that the high degree of change in temperature and humidity of the forest and the surrounding area due to artificial linear development such as roads will affect the growth of trees. This results could serve as a basis for studying the quantitative scope of linear development affecting forest growth and for managing forest change caused by linear development.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.807-820
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• 2023

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.

• Journal of Environmental Health Sciences
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• v.17 no.2
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• pp.54-66
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• 1991

As a result of measuring illumination and making up a question at home visit directly by investigator who trained over twenty days period from October 4 to 24, 1990, in order to render help which illumination problem against house, society against eyes or framing of health instruction potgram by seizing natural lighting actual conditions of house and actual conditions of wearing spectacles and by investigating interrelationship, I can summarize as follows. 1) In property of investigation subject, woman 66.9%, In an age, the twenties was largest of 27.4%, the forties was 20.2%, the fifties was 18.6%, the thirties was 17.4%. In academic career, those of upper secondary school grauates was largest of 28.6%, those who possess university career was 25.9%, those who middle school career was 20.9%, decoding of Korean alphabet was 2%. 2) By a residence area, a big city was 43.3%, farming and fishing villages were 20.3%, the rest was a small town and the administrative office of town, township. In positon of house, the middle area was 43.6%, resident of suburb area was 38.0%. In form of house, a Korean-style house was 40.8%, a western-style house was 34.8%, an apartment house was 11.0%. In the a standard of living, the middle classes 77.2%, the lower classes were 15.3%. In residential house unit of area, from 21 to 30 unit of area was largest of 31.5%, from 10 to 20 unit of area was 19.9%, from 31 to 40 was 18.7%. 3) The wearing spectacles rate of study user was 44.1%. By the area, those who wearing spectacles was more than a half of 50.8% in the resident of big city area. As passing from the farm area to the city, that is being resident of big city was high wearing spectacles rate. In position of house, as being residence in central street showed high wearing spectacles rate. (central street was 51.5%, the middle area was 44.5% and the suburb area was 40.1%.) It seemed similarity difference a variable by position of house from wearing spectacles in standard of 1%. By form of house, wearing spectacles rate those who resident in apartment house was 49.5%, that rate those who resident in a western-style house was high of 49.0%, that rate those who resident in a Korean-style house was the lowest 39.0%. By social position of resident in room, in students case who study showed very high, as university students were very high of 62.3% idn wearing spectacles rate, middle and high school students 'were 50.0%, members of society were 47.6%, workers 20.3%. It seemed similarity difference from academic career in standard of 1%. By an age, the thirties was high of 54.1% in wearing spectacles rate, the twenties was 43.2%, the teenage was the lowest of 11.8%. 4) In illumination of study, over 200Lux was high of 40.1%. but below 99Lux which inappropriate illumination to see the books was 32.4%. Average by area, below 99Lux was 22.7% and over 400Lux was 50.0% in case of wooden floor. As examine by area, below 99Lux was high of 27.0% a case of wooden floor in the big city area, it was not good in illumination passing from the farm area(15.0%) to the city(19.0%). Average illlumination by area of the main living room below 99Lux was high of 37.5%, less than 200Lux was 58.5% of whole. In general, illumination of the main livingroom was inappropriate. By area, the big city was 32.5% below 99Lux, the middle and small city area were 33.8%, town and township area were 45.0%, farming and fishing area were 42.8%. By area, in the big city, illumination of study was 52.5% over 200Lux and 28.9% below 99Lux. In case of the middle and small city, study user of below 99Lux was 38.8% and over 200Lux was 46.9%. In case of the seat of town township, below 99Lux was 34.1% and over 200Lux was 39.7%. In case of farming and fishing area, illumination of study was 33.4% below 99Lux and 48.4% over 200Lux. It tends to high rate of inappropriate illumination. 5) By position of house, in case of wooden floor, less than 100Lux was 24.5% in central street. It was bad illumination than others position of house. In case of the main livingroom, less than 100Lux was 40.4% in the suburb area. It was bad iliumnation than others position of house. In case of study, less than 100Lux was 35.4% in the middle area, it was worse in illumination. In case of the main living room, is seemed similarity difference in standard of 1%. 6) By form of house, in case of wooden floor, illumination of less than 100Lux was 23.8% in a western-style house, it was bad illumination than others form of house. In case of the main livingroom, illumination of less than 100Lux was 47.4% in a Korean-style house, it was remarkably bad illumination than others form of house. In case of study, a Korean-style house was 38.8%, it was very bad illumination than others form of house. In case of the main livingroom and study, it seemed similatrity difference each as P < 0.01 and P < 0.05 in standard of 1%. 7) The wearing spectacles rate of those who use room of illumination over 400Lux was 40.7%, and that of those who use room of illumination less than 100Lux was 28.1%. It seemed similarity differecce in standard of 1%. 8) In period of wearing spectacles, 21.3% of total investigator-highest-was from before five years, 8.6% was from before three years. Among those who use of illumintion less than 99Lux, 34.0% began to wear spectacles from before two years 31.7% was from before five years, 30.3% was from before four years. It seemed similarity difference from period of wearing spectacles by illumination in standard of 1 %. 9) Among cause which sight grow worse, the first was that it was each 33.2% and 27.4% in response rate because watch TV nearly to wearing spectacles person and non-wearing person. The second was that a lot of seeing books was 25.3% in wearing spectacles person and response rate for dark illumination was 7.4% in nonwearing spectacles person. It seemed similarity difference in standard of 1%. (P < 0.01). 10) In experience which take medicine good for eyes, it was 50.1% in wearing spectacles person and 8.5% in non-wearing spectacles person. It seemed similarity difference in standard of 1%(P < 0.01). As we have seen above, inappropriate illumination can be a cause of wearing spectacles. Nevertheless, actually, is realities to indifferent against illumination of house. So it must learn knowledge about health obstacle of illumination through society instruction and school eduction against students as well as general residents. In case that natural lighting is inappropriate structural of house, we must be able to maintain appropriate illumination through artificial illumination. And so eyes which is core of human life have to be protected, related the authorities, related group, and all health medical personnel will organically cooperate with and make efforts.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.

• Korean Journal of Agricultural Science
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• v.12 no.2
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• pp.206-241
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• 1985

Attempts to search infection period, infection speed in the tissue of neck blast of rice plant, location of inoculum source and effects of several conditions about the leaf sheath of rice plants for neck blast incidence have been made. 1. The most infectious period for neck blast incidence was the booting stage just before heading date, and most of necks have been infected during the booting stage and on heading date. But $Indica{\times}Japonica$ hybrid varieties had shown always high possibility for infection after booting stage. 2. Incubation period for neck blast of rice plants under natural conditions had rather a long period ranging from 10 to 22 days. Under artificial inoculation condition incubation period in the young panicle was shorter than in the old panicle. Panicles that emerged from the sheath of flag leaf had long incubation period, with a low infection rate and they also shown slow infection speed in the tissue. 3. Considering the incubation period of neck blast of rice plant, we assumed that the most effective application periods of chemicals are 5-10 days for immediate effective chemicals and 10-15 days for slow effective chemicals before heading. 4. Infiltration of conidia into the leaf sheath of rice plant carried out by saturation effect with water through the suture of the upper three leaves. The number of conidia observed in the leaf sheath during the booting stage were higher than those in the leaf sheath during other stages. Ligule had protected to infiltrate of conidia into the leaf sheath. 5. When conidia were infiltrated into the leaf sheath, the highest number of attached conidia was observed on the panicle base and panicle axis with hairs and degenerated panicle, which seemed to promote the infection of neck blast. 6. The lowest spore concentration for neck blast incidence was variable with rice varietal groups. $Indica{\times}Japonica$ hybrid varieties were infected easily compared to the Japonica type varieties, especially. The number of spores for neck blast incidence in $Indica{\times}Japonica$ hybrid varieties was less than 100 and disease index was higher also in $Indica{\times}Japonica$ hybrid than in Japonica type varieties. 7. Nitrogen content and silicate content were related with blast incidence in necks of rice plants in the different growing stage changed during growing period. Nitrogen content increased from booting stage to heading date and then decreased gradually as time passes. Silicate content increased from booting stage after heading with time. Change of these content promoted to increase neck blast infection. 8. Conidia moved to rice plant by ascending and desending dispersal and then attached on the rice plant. Conidia transfered horizontally was found very negligible. So we presumed that infection rate of neck blast was very low after emergence of panicle base from the leaf sheath. Also ascending air current by temperature difference between upper and lower side of rice plant seemed to increase the liberation of spores. 9. Conidial number of the blast fungus collected just before and after heading date was closely related with neck blast incidence. Lesions on three leaves from the top were closely related with neck blast incidence, because they had high potential for conidia formation of rice blast fungus and they were direct inoculum sources for neck blast. 10. The condition inside the leaf sheath was very favorable for the incidence of neck blast and the neck blast incidence in the leaf sheath increased as the level of fertilizer applied increased. Therefore, the infection rate of neck blast on the all panicle parts such as panicle base, panicle branches, spikelets, nodes, and internodes inside the leaf sheath didn't show differences due to varietal resistance or fertilizers applied. 11. Except for others among dominant species of fungi in the leaf sheath, only Gerlachia oryzae appeared to promote incidence of neck blast. It was assumed that days for heading of varieties were related with neck blast incidence.

• Journal of Global Scholars of Marketing Science
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• v.18 no.4
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• pp.1-32
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• 2008

Brown and Dacin (1997) have investigated the relationship between corporate associations and product evaluations. Their study focused on the effects of associations with a company's corporate ability (CA) and its corporate social responsibility (CSR) on consumers' product evaluations. Their study has found that both of CA and CSR influenced product evaluation but CA association has a stronger effect than CSR associations. Brown and Dacin (1997) have, however, claimed that there are few researches on how corporate association impacts product responses. Accordingly, some of researchers have found the variables to moderate or to mediate the relationship between the corporate association and the product responses. In particular, there has been existed a few of studies that tested the influence of the reputation on the product-relevant perceived risk, but the effects of two types of the corporate association on the product-relevant perceived risk were not identified so far. The primary goal of this article is to identify and empirically examine some variables to moderate the effects of CA association and CSR association on the perceived risk of the product. In this articles, we take the concept of the corporate associations that Brown and Dacin (1997) had proposed. CA association is those association related to the company's expertise in producing and delivering its outputs and CSR association reflected the organization's status and activities with respect to its perceived societal obligations. Also, this study defines the risk, which is the uncertainty or loss of the product and corporate that consumers have taken in a particular purchase decision or after having purchased. The risk is classified into product-relevant performance risk and financial risk. Performance risk is the possibility or the consequence of a product not functioning at some expected level and financial risk is the monetary loss one perceives to be incurring if a product does not function at some expected level. In relation to consumer's knowledge, expert consumers have much of the experiences or knowledge of the product in consumer position and novice consumers does not. The model tested in this article are shown in Figure 1. The model indicates that both of CA association and CSR association influence on performance risk and financial risk. In addition, the effects of CA and CSR are moderated by product category knowledge (product knowledge) and product category involvement (product involvement). In this study, the relationships between the corporate association and product-relevant perceived risk are hypothesized as the following form. For example, Hypothesis 1a($H_{1a}$) is represented that CA association has a positive influence on the performance risk of consumer. Also, the hypotheses that identified some variables to moderate the effects of two types of corporate association on the perceived risk of the product are laid down. One of the hypotheses of the interaction effect is Hypothesis 3a($H_{3a}$), it is described that consumer's knowledges of the product moderates the negative relationship between CA association and product-relevant performance risk. A field experiment was conducted in order to examine our model. The company tested was not real but imagined to meet the internal validity. Water purifiers were used for our study. Four scenarios have been developed and described as the imaginary company: Type A with both of superior CA and CSR, Type B with superior CSR and inferior CA, Type C with superior CA and inferior CSR, and Type D with both inferior of CA and CSR. The respondents of this study were classified into four groups. One type of four scenarios (Type A, B, C, or D) in its questionnaire was given to the respondent who filled out questions. Data were collected by means of a self-administered questionnaire to the respondents, chosen in convenience. A total of 300 respondents filled out the questionnaire but 207 were used for further analysis. Table 1 indicates that the scales in this study are reliable because the range of coefficients of Cronbach's $\alpha$ are from 0.85 to 0.92. The composite reliability is in the range of 0,85 to 0,92 and average variance extracted is in 0.72-0.98 range that is higher than the base level of 0.6. As shown in Table 2, the values for CFI, NNFI, root-mean-square error approximation (RMSEA), and standardized root-mean-square residual (SRMR) are acceptably close to the standards suggested by Hu and Bentler (1999):.95 for CFI and NNFI,.06 for RMSEA, and.08 for SRMR. We also tested discriminant validity provided by Fornell and Larcker (1981). As shown in Table 2, we found strong evidence for discriminant validity between each possible pair of latent constructs in all samples. Given that these batteries of overall goodness-of-fit indices were accurate and that the model was developed on theoretical bases, and given the high level of consistency across samples, this enables us to proceed the previously defined scales. We used the moderated hierarchical regression analysis to test the influence of the corporate association(CA and CSR associations) on product-relevant perceived risk(performance and financial risks) and to identify the variables moderating the relationship between the corporate association and product-relevant performance risk. In this study, dependent variables are performance and financial risk. CA and CSR associations are described the independent variables. The moderating variables are product category knowledge and product category involvement. The results are, as expected, found that CA association has statistically a significant influence on the perceived risk of the product, but CSR association does not. Product category knowledge and involvement moderate the relationship between the CA association and the perceived risk of the product. However, the effect of CSR association on the perceived risk of the product is not moderated by the consumers' knowledge and involvement. For this result, it is necessary for a corporate to inform its customers CA association more than CSR association so that they could be felt to be the reduction of the perceived risk. The important theoretical contribution of this research is the meanings that two types of corporate association that Brown and Dacin(1997), and Brown(1998) have proposed replicated the difference of the effects on product evaluation. According to Hunter(2001), it was an important affair to accomplish the validity of a particular study and we had to take about ten studies to deduce a strict study. Next, there is the contribution of the this study to find that the effects of corporate association on the perceived risk of the product are varied by the moderator variables. In particular, the moderating effect of knowledge on the relationship between corporate association and product-relevant perceived risk has not been tested in Korea. In the managerial implications of this research, we suggest the necessity to stress the ability that corporate manufactures the product well(CA association) than the accomplishment of corporate's social obligation(CSR association). This study suffers from various limitations that imply future research directions. The moderating effects of product category knowledge and involvement on the relationship between corporate association and perceived risk need to be replicated. Next, future research could explore whether the mediated effects of the perceived risk has the relationship between corporate association and consumer's product purchase. In addition, to ensure the external validity of the study will be needed to use realistic company, not artificial.