• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.1-12
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• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.

• Journal of Biosystems Engineering
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• v.10 no.1
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• pp.54-68
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• 1985

It is desirable to collect the solar thermal energy at relatively high temperature in order to minimize the size of thermal storage system and to enlarge the scope of solar thermal energy utilization. So far the concentrating solar collector has been developed to collect solar thermal energy at relatively high temperature, but it has some difficulties in maintaining the volumetric body of solar collector for long term utilization. On the other hand, the flat-plate solar collector has been developed to collect the solar thermal energy at low temperature, and it has advantages in maintaining the system for long term utilization, since it's thickness is thin and not volumetric. In this study, to develop a solar collector that has both advantages of collecting solar thermal energy at high temperature and fixing conveniently the collector system for long term period, a cylindrical parabolic concentrating solar collector was designed, which has two rows of parabolic reflectors and thin thickness such as the flat-plate solar collector, maintaining the optical form of concentrating solar collector. The characteristics of the concentrating parabolic solar collector newly designed was analysed and the results are summarized as follows; 1. The temperature of the air enclosed in solar collector was all the same as $50^{\circ}C$ in both cases of the open and closed loop, and when the heat transfer fluid was not circulated in tubular absorber, the maximum surface temperature of the absorber was $118-120^{\circ}C$, this results suggested that the heat transfer fluid could be heated up to $118^{\circ}C$. 2. In case of longitudinal installation of the solar collector, the temperature difference of heat transfer fluid between inlet and outlet was $4^{\circ}-6^{\circ}C$ at the flow rate of $110-130{\ell}/hr$, and the collected solar energy per unit area of collector was $300-465W/m^2$. 3. The collected solar energy per unit area for 7 hours was 1960 Kcal/$m^2$ for the open loop and 220 Kcal/$m^2$ for the closed loop. Therefore it is necessary to combine the open and closed loop of solar collectors to improve the thermal efficiency of solar collector. 4. The thermal efficiency of the solar collector (C.P.C.S.C.) was proportional to the density of solar radiation, indicating the maximum thermal efficiency ${\eta}_{max}=58%$ with longitudinal installation and ${\eta}_{max}=45%$ with lateral installation. 5. The thermal efficiency of the solar collector (C.P.C.S.C.) was increased in accordance with the increase of flow rate of heat transfer fluid, presenting the flow rate of $110{\ell}/hr$ was the value of turning point of the increasing rate of the collector efficiency, therefore the flow rate of $110{\ell}/hr$ was considered as optimum value for the test of the solar collector (C.P.C.S.C.) performance when the heat transfer fluid is a liquid. 6. In both cases of longitudinal and lateral installation of the solar collector (C.P.C.S.C.), the thermal efficiency was decreased linearly with an increase in the value of the term ($T_m-T_a$)/Ic and the increasing rate of the thermal efficiency was not effected by the installation method of solar collector.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Journal of Bio-Environment Control
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• v.27 no.2
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• pp.166-172
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• 2018

This study set out to conduct a field survey with smart greenhouse-based farms in seven types to figure out the actual state of smart greenhouses distributed across the nation before selecting a system to implement an optimal greenhouse environment and doing a research on higher productivity based on data related to crop growth, development, and environment. The findings show that the farms were close to an intelligent or advanced smart farm, given the main purposes of leading cases across the smart farm types found in the field. As for the age of farmers, those who were in their forties and sixties accounted for the biggest percentage, but those who were in their fifties or younger ran 21 farms that accounted for approximately 70.0%. The biggest number of farmers had a cultivation career of ten years or less. As for the greenhouse type, the 1-2W type accounted for 50.0%, and the multispan type accounted for 80.0% at 24 farms. As for crops they cultivated, only three farms cultivated flowers with the remaining farms growing only fruit vegetables, of which the tomato and paprika accounted for approximately 63.6%. As for control systems, approximately 77.4% (24 farms) used a domestic control system. As for the control method of a control system, three farms regulated temperature and humidity only with a control panel with the remaining farms adopting a digital control method to combine a panel with a computer. There were total nine environmental factors to measure and control including temperature. While all the surveyed farms measured temperature, the number of farms installing a ventilation or air flow fan or measuring the concentration of carbon dioxide was relatively small. As for a heating system, 46.7% of the farms used an electric boiler. In addition, hot water boilers, heat pumps, and lamp oil boilers were used. As for investment into a control system, there was a difference in the investment scale among the farms from 10 million won to 100 million won. As for difficulties with greenhouse management, the farmers complained about difficulties with using a smart phone and digital control system due to their old age and the utter absence of education and materials about smart greenhouse management. Those difficulties were followed by high fees paid to a consultant and system malfunction in the order.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Development and Reproduction
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• v.2 no.2
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• pp.179-187
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• 1998

The effects of prostaglandins in hatching and implantation have been studied but the results were various, and those are not well known by the embryonic stage. The present study examined the effects of prostaglandin $E_2$(PG $E_2$) and prostaglandin $F_2$$_{\alpha}$ (PG $F_2$$_{\alpha}$) on the expansion and hatching of mouse embryos by embryonic stage. Also we tried to measure the concentration of prostaglandins of morula, expanded, and hatching embryos. In early morula stage embryos, high concentration of PG $E_2$(>100$\mu$M) showed cytotoxicity but PG $F_2$$_{\alpha}$ did not. The hatching was inhibited all groups but not gave negative effects on expansion. In 84 hr and 96 hr stage embryos, the hatching rate was decreased at all treatment groups but not inhibited the expansion. When combine prostaglandin with indomethacin, the hatching rate was increased significantly compared to the prostaglandin-treated groups, and as lower and lower the PG $E_2$ concentration, the hatching rate increased to the control level. The embryonic synthesis of PG $E_2$ increased dramatically but that of PG $F_2$$_{\alpha}$ increased gradually. PG $E_2$ showed cytotoxicity at early stage embryos much than late stage embryos, but PG $F_2$$_{\alpha}$ did not. Hatching was inhibited by the high PG $F_2$$_{\alpha}$ concentration. It is suggested that the inhibition of hatching might be at resulted from cytotoxicity of PG $E_2$ on embryo. However, it is thought that the mechanisms of inhibition of hatching are different between PG $E_2$ and PG $F_2$$_{\alpha}$. In conclusion, it can be suggested that PG $E_2$ and PG $F_2$$_{\alpha}$ concerned with the expansion and hatching, and their effects on hatching were different by the embryonic stage.$/ concerned with the expansion and hatching, and their effects on hatching were different by the embryonic stage.

• Survey Research
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• v.13 no.1
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• pp.135-158
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• 2012

Korean telephone surveys have been based on landline telephone directory or RDD(Random Digit Dialing) method. These days, however, there has been an increase of the households with no landline, or households with the line but not willing to register in the directory. Moreover, it is hard to contact young people or office workers who are usually staying out of home in the daytime. Due to these issues above, the predictability of election polls gets weaker. Especially, low accessibility to those who stay out of home when the poll's done, results in predictions with positive inclination toward conservatism. A solution to resolve this problem is to contact respondents by using both mobile and landline phones-via landline phone to those who are at home and via mobile phone to those who are out of home in the daytime(Mixed Mode Survey, hereafter MMS). To conduct MMS, 1) we need to obtain the sampling frames for the landline and mobile surveys, and 2) we need to decide the proportion of sample size of both. In this paper, we propose a heuristic method for conducting MMS. The method uses RDD for the landline phone survey, and the access panel list for the mobile phone survey. The proportion of sample sizes between landline and mobile phones are determined based on the 'Lifestyle and Time Use Study' conducted by Statistics Korea. As a case study, 4 election polls were conducted in the periods of the special election for the mayor of Seoul on Oct 26th, 2011. From the initial 3 polls, reactions and responses regarding the issues raised during the survey period were appropriately covered, and the final poll showed a very close prediction to the real election result.

• Survey Research
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• v.13 no.1
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• pp.61-87
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• 2012

This research is a part of a fundamental research to form the topographic map of the South Korean society's cultural capital, based on large scale research data. Its purpose is to suggest suitable questions for today's Korean society as well as to compare with previous data accumulated from other nations. For this, this research is to establish theoretical background through critical study on the extensive literature on domestic and foreign cultural capital and collect measures, questionnaires, and data used in important literature and surveys. Based on this, the major domains and levels that should be dealt in the questionnaire were chosen, literature review was conducted for each field; experts were investigated in order to develop questions more suitable for the Korean society considering each domain and level, and qualitative research on the subjects were conducted. This research as seen through the above processes, music genres and culture activities were chosen as major domains, "high/popular" level and "consumption/production" level were chosen as items, and specific items were composed considering Korea's distinct characteristics. Each of these items combine and complement the three aspects of measuring cultural capital(preference, participation, perception), which have been used incoherently in previous researches in measuring the level of possession in cultural capital. This led to developing questions such as the level of liking each item(preference), the level of participating in each item(participation), the level of luxuriousness in each item(perception), and the level of stylishness in each item(perception). This research holds significance in that it critically examines the vast amount of questionnaires used in the past for cultural capital research, provides a large framework to find Korean cultural capital by adding items considering Korea's distinct characteristics, and provides groundwork to fill in the non-Western gap in the discussion of cultural capital, which has been based on the West.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.4 no.4
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• pp.113-125
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• 1984

Water supply has been mainly dependent on the construction of the dams in Korea. It is difficult, however, to continue to construct dams for many reasons, such as the decrease of construction sites, the increase of construction costs, the compensation of residents in flooded areas, and the environmental effects. Water demands have increased and are expected to continue increasing due to the concentration of people in the cities, the rise of the living standard, and rapid industrial growth. It is acutely important to find countermeasures such as development of ground water, desalination, and recycling of waste water to cope with increasing water demands. Recycling waste water includes all means of supplying non-potable water for their respective usages with proper water quality which is not the same quality as potable water. The usages of the recycled water include toilet flushing, air conditioning, car washing, yard watering, road cleaning, park sprinkling, and fire fighting, etc. Raw water for recycling is obtained from drainage water from buildings, toilets, and cooling towers, treated waste water, polluted rivers, ground water, reinfall, etc. The water quantity must be considered as well as its quality in selecting raw water for the recycling. The types of recycling may be classified roughly into closed recycle systems and open recycle systems, which can be further subdivided into individual recycle systems, regional recycle systems and large scale recycle system. The treatment methods of wastewater combine biochemical and physiochemical methods. The former includes activated sludge treatment, bio-disc treatment, and contact aeration treatment, and the latter contains sedimentation, sand filtration, activated carbon adsorption, ozone treatment, chlorination, and membrane filter. The recycling patterns in other countries were investigated and the effects of the recycling were divided into direct and indirect effects. The problems of water reuse in recycle patterns were also studied. The problems include technological, sanitary, and operational problems as well as cost and legislative ones. The duties of installation and administrative organization, structural standards for reuse of water, maintenance and financial disposal were also studied.