• The Korean Journal of Food And Nutrition
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• v.27 no.3
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• pp.414-420
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• 2014

This study was performed to increase the yield and to reduce the processing times for the preparation to improve the productivity and quality of rice jochung, a traditional food in Korea. In order to evaluate the quality characteristics and yield of jochung, the viscosity, color value, mineral contents and the sensory evaluation were measured. Jochung is prepared from steamed rice (STR), wet-milled rice flour (WRF) and dry-milled rice flour (DRF) by processing methods of rice and reacting times (6 hours or 13 hours) of liquefaction and saccharification. There is commonly added liquefying enzyme for rice liquefaction (0.4%/10 kg rice, at $85{\sim}90^{\circ}C$ for 1 hour or 4 hours) and saccharogenic enzyme with malt (2.5% or 4.5%/10 kg rice, at $56{\sim}60^{\circ}C$ for 5 hours or 9 hours). The inner structural properties of WRF showed the more distinct shape regular structure of uncombined starch particles but the DRF closely maintained particles of rice flour observed by SEM. If processing times for liquefation and saccharification were reduced from 13 hours to 6 hours, the yield of jochungs prepared with WRF increased 8%, the DRF 7%, and the STR 3% respectively and the sensory evaluation as well as color values and overall desirability received high scores. The viscosity, color a and b values of jochung processed with WRF for 6 hours were lower than that processed for 13 hours. The viscosity and color a, b value and Ca content were decreased in the jochung processed with WRF or DRF for 6 hours, but Mg, P and K were increased than that of STR. Jochung processed by 0.4% liquefying enzyme and 2.5% malt with WRF for 6 hours will increase the yield, save manufacturing times and costs and will thereby enable cost-effective techniques.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.193-204
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• 2015

An array DBMS has been expected widely from scientists because it is convenient to store and analyze the data of array type. In this paper, we describe how to handle satellite remote-sensing images in the array DBMS. However, previous works in their visualization have two problems as follows. First, the images are visualized as a state of distorted by the curvature of the earth. Second, it is difficult to apply the results of visualization by pre-written queries to other analyses. Therefore, this paper proposes a three dimensional visualization method of satellite remote-sensing images, not traditional 2D visualization. Our research contents are as follows. First, we describe how to store, process, and analyze the satellite remote-sensing images in the array DBMS. Second, we propose a three-dimensional visualization method for their processed outputs. Lastly, our contributions can be summarized that we propose a method of handling satellite remote-sensing images in the array DBMS and their 3D visualization techniques. It is also expected that their use be available widely in many industrial areas.

• Journal of Digital Convergence
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• v.16 no.1
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• pp.231-242
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• 2018

The efficient management of the Length of Stay(LOS) is important in hospital. It is import to reduce medical cost for patients and increase profitability for hospitals. In order to efficiently manage LOS, it is necessary to develop an artificial intelligence-based prediction model that supports hospitals in benchmarking and reduction ways of LOS. In order to develop a predictive model of LOS for acute stroke patients, acute stroke patients were extracted from 2013 and 2014 discharge injury patient data. The data for analysis was classified as 60% for training and 40% for evaluation. In the model development, we used traditional regression technique such as multiple regression analysis method, artificial intelligence technique such as interactive decision tree, neural network technique, and ensemble technique which integrate all. Model evaluation used Root ASE (Absolute error) index. They were 23.7 by multiple regression, 23.7 by interactive decision tree, 22.7 by neural network and 22.7 by esemble technique. As a result of model evaluation, neural network technique which is artificial intelligence technique was found to be superior. Through this, the utility of artificial intelligence has been proved in the development of the prediction LOS model. In the future, it is necessary to continue research on how to utilize artificial intelligence techniques more effectively in the development of LOS prediction model.

• The Journal of Society for e-Business Studies
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• v.8 no.4
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• pp.69-88
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• 2003

In high dimensional data, most of clustering algorithms tend to degrade the performance rapidly because of nature of sparsity and amount of noise. Recently, partial dimensional clustering algorithms have been studied, which have good performance in clustering. These algorithms select the dimensional data closely related to clustering but discard the dimensional data which are not directly related to clustering in entire dimensional data. However, the traditional algorithms have some problems. At first, the algorithms employ grid based techniques but the large amount of grids make worse the performance of algorithm in terms of computational time and memory space. Secondly, the algorithms explore dimensions related to clustering using k-medoid but it is very difficult to determine the best quality of k-medoids in large amount of high dimensional data. In this paper, we propose an efficient partial dimensional clustering algorithm which is called CLIP. CLIP explores dense regions for cluster on a certain dimension. Then, the algorithm probes dense regions on a next dimension. dependent on the dense regions of the explored dimension using incremental projection. CLIP repeats these probing work in all dimensions. Clustering by Incremental projection can prune the search space largely and reduce the computational time considerably. We evaluate the performance(efficiency, effectiveness and accuracy, etc.) of the proposed algorithm compared with other algorithms using common synthetic data.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.5
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• pp.219-230
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• 2017

General-Purpose Graphics Processing Units (GPGPUs) build massively parallel architecture and apply multithreading technology to explore parallelism. By using programming models like CUDA, and OpenCL, GPGPUs are becoming the best in exploiting plentiful thread-level parallelism caused by parallel applications. Unfortunately, modern GPGPU cannot efficiently utilize its available hardware resources for numerous general-purpose applications. One of the primary reasons is the inefficiency of existing warp/thread block schedulers in hiding long latency instructions, resulting in lost opportunity to improve the performance. This paper studies the effects of hardware thread scheduling policy on GPGPU performance. We propose a novel warp scheduling policy that can alleviate the drawbacks of the traditional round-robin policy. The proposed warp scheduler first classifies the warps of a thread block into two groups, warps with long latency and warps with short latency and then schedules the warps with long latency before the warps with short latency. Furthermore, to support the proposed warp scheduler, we also propose a supplemental technique that can dynamically reduce the number of streaming multiprocessors to which will be assigned thread blocks when encountering a high contention degree at the memory and interconnection network. Based on our experiments on a 15-streaming multiprocessor GPGPU platform, the proposed warp scheduling policy provides an average IPC improvement of 7.5% over the baseline round-robin warp scheduling policy. This paper also shows that the GPGPU performance can be improved by approximately 8.9% on average when the two proposed techniques are combined.

• Informatization Policy
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• v.24 no.4
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• pp.3-16
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• 2017

With the development of social media services in the era of Web 2.0, the public opinion formation site has been partially shifted from the traditional mass media to social media. This phenomenon is continuing to expand, and public opinions on government polices created and shared on social media are attracting more attention. It is particularly important to grasp public opinions in policy formulation because setting up educational policies involves a variety of stakeholders and conflicts. The purpose of this study is to explore public opinions about education-related policies through an empirical analysis of social media documents on education policies using opinion mining techniques. For this purpose, we collected the education policy-related documents by keyword, which were produced by users through the social media service, tokenized and extracted sentimental qualities of the documents, and scored the qualities using sentiment dictionaries to find out public preferences for specific education policies. As a result, a lot of negative public opinions were found regarding the smart education policies that use the keywords of digital textbooks and e-learning; while the software education policies using coding education and computer thinking as the keywords had more positive opinions. In addition, the general policies having the keywords of free school terms and creative personality education showed more negative public opinions. As much as 20% of the documents were unable to extract sentiments from, signifying that there are still a certain share of blog posts or tweets that do not reflect the writers' opinions.

• Journal of Pharmaceutical Investigation
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• v.30 no.3
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• pp.191-199
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• 2000

Genomics is providing targets faster than we can validate them and combinatorial chemistry is providing new chemical entities faster than we can screen them. Historically, the drug discovery cascade has been established as a sequential process initiated with a potency screening against a selected biological target. In this sequential process, pharmacokinetics was often regarded as a low-throughput activity. Typically, limited pharmacokinetics studies would be conducted prior to acceptance of a compound for safety evaluation and, as a result, compounds often failed to reach a clinical testing due to unfavorable pharmacokinetic characteristics. A new paradigm in drug discovery has emerged in which the entire sample collection is rapidly screened using robotized high-throughput assays at the outset of the program. Higher-throughput pharmacokinetics (HTPK) is being achieved through introduction of new techniques, including automation for sample preparation and new experimental approaches. A number of in vitro and in vivo methods are being developed for the HTPK. In vitro studies, in which many cell lines are used to screen absorption and metabolism, are generally faster than in vivo screening, and, in this sense, in vitro screening is often considered as a real HTPK. Despite the elegance of the in vitro models, however, in vivo screenings are always essential for the final confirmation. Among these in vivo methods, cassette dosing technique, is believed the methods that is applicable in the screening of pharmacokinetics of many compounds at a time. The widespread use of liquid chromatography (LC) interfaced to mass spectrometry (MS) or tandem mass spectrometry (MS/MS) allowed the feasibility of the cassette dosing technique. Another approach to increase the throughput of in vivo screening of pharmacokinetics is to reduce the number of sample analysis. Two common approaches are used for this purpose. First, samples from identical study designs but that contain different drug candidate can be pooled to produce single set of samples, thus, reducing sample to be analyzed. Second, for a single test compound, serial plasma samples can be pooled to produce a single composite sample for analysis. In this review, we validated the issue whether the second method can be applied to practical screening of in vivo pharmacokinetics using data from seven of our previous bioequivalence studies. For a given drug, equally spaced serial plasma samples were pooled to achieve a 'Pooled Concentration' for the drug. An area under the plasma drug concentration-time curve (AUC) was then calculated theoretically using the pooled concentration and the predicted AUC value was statistically compared with the traditionally calculated AUC value. The comparison revealed that the sample pooling method generated reasonably accurate AUC values when compared with those obtained by the traditional approach. It is especially noteworthy that the accuracy was obtained by the analysis of only one sample instead of analyses of a number of samples that necessitates a significant man-power and time. Thus, we propose the sample pooling method as an alternative to in vivo pharmacokinetic approach in the selection potential lead(s) from combinatorial libraries.

• Journal of Conservation Science
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• v.31 no.2
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• pp.125-130
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• 2015

Specific techniques and materials in conservation of traditional lacquer has not been transmitted. This study aims to compare the basic characteristics of the filler which used in the base layer of lacquer conservation. Tohoe(a mixture of lacquer and Clay) and the three kinds of additives which is mixed with Tohoe and Goksu(a mixture of lacquer, wood powder and rice starch) are estimated in drying rate, impact resistance, abrasion. Among those samples, the more amount of clay causes fast dryness speed and worse cracks on the surface. The impact resistance is weakened at high amount of clay. There is no significant differences of impact resistance between both additives that is mixed with the samples and additives. The samples that are mixed with Goksu and additives show high impact resistance. In the polishing test, the more amount of filling powders show higher grinding degree and the sample that are mixed with wood powder and charcoal show higher degree as well. The highest grinding degree is Maekchil and Goksu but the lowest one is the sample of the rooftile powder mixture.

• Journal of Distribution Science
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• v.10 no.5
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• pp.29-36
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• 2012

This paper aims to establish a Fengshui-based marketing model that companies engaged in selling dwelling spaces can utilize to increase their sales. The study is based on an investigation of appraisal statements and analysis techniques used in Fengshui. The Fengshui marketing model can be used for corporate advertising, sales promotions, public relations events, and for framing an overall marketing strategy according to changing consumer demand. As a sales promotion strategy, it can be used to influence consumer psychology and behavior. Although this study is limited to the all-pervasive advertising and marketing of houses by construction companies under installment plans, the Fengshui marketing method can also be used for the sale of store locations, space for product display, and so on. Initially, I analyze living spaces according to traditional Fengshui theory, and subsequently apply the modern method to study topographical space structures and geomagnetism disturbances. I present a standard form for writing the Fengshui appraisal statement based on the objective analytical method of Fengshui. With its shortcomings remedied, the appraisal statement can lead to high-quality advertising and increased valuations because it is based on objective data analysis and systematic evaluation of houses. In brief, I have designed the Fengshui marketing model as a sales promotion technique for the housing industry. I believe this study will contribute to the application of Fengshui in the housing industry's sales promotion efforts through high-quality advertising. Future research should evaluate Fengshui marketing in the housing industry based on case studies. Research questions to be addressed could include how Fengshui marketing has affected installment sales of houses and how Fengshui architectural practices affect general well-being. These studies would help propagate Fengshui marketing by validating its effectiveness. In addition, case studies should be undertaken to consider the practical applications of Fengshui marketing, how it can contribute to maximizing a company's image and profits, and how it can promote customer satisfaction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.373-376
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• 2018

Traditional paprika smart farm systems are often harmful to paprika growth because they are set to follow the values of several sensors to the reference value, so the system is often unable to make optimal judgement. Using decision tree techniques, the expert system for the paprika smart farm is designed to create a control system with a decision-making structure similar to that of farmers using data generated by factors that depend on their surroundings. With the current smart farm control system, it is essential for farmers to intervene in the surrounding environment because it is designed to follow sensor values to the reference values set by the farmer. To solve this problem even slightly, it is going to obtain environmental data and design controllers that apply decision tree method. The expert system is established for complex control by selecting the most influential environmental factors before controlling the paprika smart farm equipment, including criteria for selecting decisions by farmers. The study predicts that each environmental element will be a standard when creating smart farms for professionals because of the interrelationships of data, and more surrounding environmental factors affecting growth.