• Journal of KIISE:Computing Practices and Letters
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• v.11 no.1
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• pp.15-25
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• 2005

Embedded systems have a set of tasks to execute. These tasks can be implemented either on application specific hardware or as software running on a specific processor. The design of an embedded system involves the selection of hardware software resources, Partition of tasks into hardware and software, and performance evaluation. An accurate estimation of execution time for extreme cases (best and worst case) is important for hardware/software codesign. A tighter estimation of the execution time bound nay allow the use of a slower processor to execute the code and may help lower the system cost. In this paper, we consider an ARM-based embedded system and developed a tool to estimate the tight boundary of execution time of a task with loop bounds and any additional program path information. The tool we developed is based on an exiting timing analysis tool named 'Cinderella' which currently supports i960 and m68k architectures. We add a module to handle ARM ELF object file, which extracts control flow and debugging information, and a module to handle ARM instruction set so that the new tool can support ARM processor. We validate the tool by comparing the estimated bound of execution time with the run-time execution time measured by ARMulator for a selected bechmark programs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2749-2756
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• 2015

Recently, Depending on the quality of data increases, the problem of time-consuming to process the image is raised by being required to accelerate the image processing algorithms, in a traditional CPU and CUDA(Compute Unified Device Architecture) based recognition system for computing speed and performance gains compared to OpenMP When character recognition has been learned by the system to measure the input by the character data matching is implemented in an environment that recognizes the region of the well, so that the font of the characters image learning English alphabet are each constant and standardized in size and character an image matching method for calculating the matching has also been implemented. GPGPU (General Purpose GPU) programming platform technology when using the CUDA computing techniques to recognize and use the four cores of Intel i5 2500 with OpenMP to deal quickly and efficiently an algorithm, than the performance of existing CPU does not produce the rate of four times due to the delay of the data of the partition and merge operation proposed a method of improving the rate of speed of about 3.2 times, and the parallel processing of the video card that processes a result, the sequential operation of the process compared to CPU-based who performed the performance gain is about 21 tiems improvement in was confirmed.

• Applied Biological Chemistry
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• v.4
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• pp.33-42
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• 1963

1. Two fermentation methods have been compared for the production of Takju (Korean native Sake). One method (S1) uses 'Nuruk' (Korean mold wheat) and the other method (S2) uses 'Nuruk' and mold rice. The chemical analysis of source material, also, have been made in order the check the quantitative changes during the fermentation. The results are summarized below: a. The rate of fermentation was slower for the S2 method than for the S1 method. However, the residual sugar in the S2 method was smaller and the amount of alcohol produced the same as with the S1 method. This was true in spite of the fact that the total initial sugar content for the S2 method was 10 percent below that of S1. b. With both the S1 and S2 methods, 80 percent of the total alcohol production was obtained with in 3 to 4 days. c. The pH value of the base material at the beginning of fermentation was markedly different between the two methods I.e.6.0 to 6.2 for S1 and 4.8 to 5.2 for S2. However, after one or two day the pH of both materials was about the same 4.0 to 4.2 and remained at this value unit the fermentation was complete. 2. Organic acids and Sugars in the source materials have been detected by the paper partition chromatography(p.p.c.) method and the followings are obtained. a. Important Organic acids are Fumalic, malic Succinic, Citric, Acetic aicds in polished rice and Fumalic, Succinic, Acetic, Citric, Malic and Oxalic acids 'Nuruk'. The same kinds of acids as in the rice are found in mold rice, However, amount of Citrice acid is markedly increased in mold rice. b. The important Sugar, Glucose, Fructose and Raffinose in polished rice, Gulcose, Fructo, se, Xylose and one which supposed to be Kojibjose in 'Nuruk'. Glucose and those supposed to be Isomaltose, Kojibiose and Sakebiose and found in rice mold, however, no Sucrose, which was exist in polished rice, was found. 3. The important Organic acids found in fermenting mash using the p.p.c. method were Lactic, Succinic and Acetic. Citric acid identified early in the fermentation, S2 method. remained throughout. However, with the S1 method Cirtic acid was detected only during the late stage of fermentation. Sugars not found in the original materials were two which supposed to be Isomaltotriose and Pentose. Maltose found at the beginning of the fermentation disappeared within one day, Isomaltose was detected throughout the period of fermentation. 4. The Somogyi method which was employed to determine the quantitative changes of sugars in the orginal meterial and mach, showed that polished rice containes in order and in largest amounts Sucrose, Glucose, Raffinose and Fructose. 'Nuruk' contained almost equal quantities of Glucose and Fructose. However, the Glucose content of the mold rice exceeded that originally in the polished rice by 25 or 30 times. Only a small quantity of free sugars was found in the mash at the end of the fermentation.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Korean Journal of Microbiology
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• v.49 no.4
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• pp.360-368
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• 2013

Recently interactive water fountains are gaining popularity in making public facilities in South Korea. The total number of interactive fountains is rapidly growing at the rate of >50% annually. In this study, we performed quantitative microbial risk assessment to estimate infection risks in children by Legionella spp. while playing in interactive fountains. The exposure dose for a given concentration of Legionella in water was calculated using water-aerosol partition rate of Legionella, exposure duration, inhalation rate, and deposit rate of aerosols in the lungs following inhalation. The dose was converted to infection risk by using the dose-response function developed for L. pneumophila. High weight and/or old children, i.e., 12-year children, running around in fountains were the highest risk group by showing >0.05 infection probability for fountain waters containing ${\geq}10^4$ CFU/L Legionella. The result supported the current guideline by Korea Centers for Disease Control and Prevention, which permits use of water with < $10^3$ CFU/L Legionella cells for all purposes. However, the results still warrant further evaluation of the guideline to accommodate risks for children because the dose-response relationship in the model was developed for healthy adults. Further risk assessment studies need to be conducted by employing dose-response model for children who generally carries weaker immune system than adults.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.3 no.2
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• pp.30-38
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• 1992

In this paper, in order to apply the $\pi$/4 shift QPSK to TCM, we propose the $\pi$/8 shift 8PSK modulation technique and the trellis-coded $\pi$/8 shift 8PSK performing signal set expansion and set partition by phase difference. In addition, the Viterbi decoder with branch metrics of the squared Euclidean distance of the first phase difference as well as the Lth phase difference is introduced in order to improve the bit error rate(BER) performance in differential detection of the trellis-coded $\pi$/8 shift 8 PSK. The proposed Viterbi decoder is conceptually the same as the sliding multiple de- tection by using the branch metric with first and Lth order phase difference. We investigate the performance of the uncoded .pi. /4 shift QPSK and the trellis-coded $\pi$/8 shift 8PSK with or without the Lth phase difference metric in an additive white Gaussian noise (AWGN) and Rayleigh fading channel using the Monte Carlo simulation. The study shows that the $\pi$/4 shift QPSK with the Trellis-code i. e. the trellis-coded $\pi$/8 shift 8PSK is an attractive scheme for power and bandlimited systems and especially, the Viterbi decoder with first and Lth phase difference metrics improves BER performance. Also, the next proposed algorithm can be used in the TC $\pi$/8 shift 8PSK as well as TC MDPSK.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.754-765
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• 2006

In XML keyword search, a search result is defined as a set of the smallest elements (i.e., least common ancestors) containing all query keywords and a granularity of indexing is an XML element instead of a document. Under the conventional index structure, all least common ancestors produced by the combination of the elements, each of which contains a query keyword, are considered as a search result. In this paper, to avoid unnecessary operations of producing the least common ancestors and reduce query process time, we describe a way to construct a partitioned index composed of several partitions and produce a search result by merging those partitions if necessary. When a search result is restricted to be composed of the least common ancestors whose depths are higher than a given minimum depth, under the proposed partitioned index structure, search systems can reduce the query process time by considering only combinations of the elements belonging to the same partition. Even though the minimum depth is not given or unknown, search systems can obtain a search result with the partitioned index, which requires the same query process time to obtain the search result with non-partitioned index. Our experiment was conducted with the XML documents provided by the DBLP site and INEX2003, and the partitioned index could reduce a substantial amount of query processing time when the minimum depth is given.

• Journal of Educational Research in Mathematics
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• v.17 no.1
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• pp.33-50
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• 2007

Positive attitude toward mathematics is gaining bigger recognition as an important contributing factor to mathematical ability. As a strategy for strengthening affective domain and betterment of mathematics teaching and loaming, classifying students by their causes for liking or disliking mathematics can be an effective way In this study the author tried to devise methods to classify students by their types of math disliking and investigate correlations between mathematical achievements and these math-disliking types from a sample group of 8th graders. To identify the types of reasons why 8th graders dislike mathematics, a questionnaire with 30 items was made firstly. Then by applying the 'Factor analysis' of SPSS, the 30 items were divided into five partitions. Through abstraction of each partition, five math-disliking types, 'Competences', 'Basics', 'Confidences', 'Usefulness', and 'Teachers' were defined. They are expected to help teachers for describing each student's tendency of math-disliking. Further, correlation coefficients between mathematical achievements and each of the five math-disliking type were investigated against 4 groups which were made from sample group by the discrimination of gender and two levels (high and low) of mathematical achievements in cognitive area. As results, the following facts were found. (i) The trends of correlations between cognitive achievement and the five math disliking types were different across the 4 groups at statistically meaningful degrees. (ii) Most of the male students who had math-disliking types were proved to be in the low achievement level. But for the female students, only 50% of students who had math-disliking types were in the low achievement level. (iii) Compared to male students, higher portion of female students had math-disliking types despite their high achievement in cognitive area.

• Journal of the East Asian Society of Dietary Life
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• v.8 no.4
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• pp.441-453
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• 1998

Varieties of the cereals produced in the Biblical times and the ways these cereal foods have been grafted into Korean food culture has been pursued, and the regulations of agricultural life and the metaphors or parables of cereals represented throughout the Bible have been studied. The word "grain" appears eight times in the KJV and one hundred seventeen times in the RSV. On the other hand, the word "corn" shows up one hundred and one times in the KJV but not in the RSV at all. Wheat, one of the earliest cultivated and the most important grains for food is mentioned fifty two times in KJV and generally the rich in those days lived on wheat in the parched state or in the form of bread. Barley, one of the staple cereal crops of Palestine is referred to thirty six times in the KJV. It was less expensive than wheat and so was used mainly for the food of such animals as horses, donkeys and cattle, but sometimes it was mixed with flour and used for making bread for men, mostly for Poor men. The spelt (in ASV and RSV) and the rie(in KJV) appearing in Exodus 9:32 is a kind of grain, which is translated into Na-mack (in Hangul Revised Bible ; HRB), Ssal-bori (in Hangul Common Translated Bible : HCB) and Ho-mil (in Hangul King James Bible; HKJB) but which should be put into Spelt-mil in Korean. The lentil appearing in Genesis 25 : 34 is translated into Pat (red-bean) and Bul-kong respectively in HRB and HCB but the same word in the Second Book of Samuel is translated into Pat in both HRB and HCB. HCB translates lentil into Bul-kong in Genesis and into Pat in the Second Book of Samuel (23:11). HCB and HKJB which put lentil into Pat in previous parts translate the word into Nok-tu in the Second Book of Samuel. The word, lentil here should be put into lentil-kong and parched pulse into "Poken-Chong-ja" not into Poken-Nok-tu. Millet which is translated into Cho should be put into Gui-jang. filches should not be classified as a grain or a cereal but as a flavor or a condiment, so the word filches should be put into So-Hoi-hyang or Hoi-hyang. Regulations in reference to agriculture are included in the Bible. That is, it is seen that the Bible regulates the observance of the sabbatical year and feasts, the partition of land, how to sow and so on. It is also observed that grains are used for metaphors or parables: wheat represents the peaceful times and straws are compared to trifles. As seen above, there should be more prudent examinations and standards for the translation of grains into Pat, Pul-kong, Nok-tu, Gui-ree, Ssal-bo-ri and Na-mack.i-ree, Ssal-bo-ri and Na-mack.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.