• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.649-655
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• 2020

This study determined effective educational strategies by investigating and analyzing the related educational demands for SMEs (small and medium-sized enterprises) in the 4th Industrial Revolution based area of smart farms. In order to derive the approprate educational strategies, Importance-Performance Analysis (IPA) and Borich's Needs Assessment Model were conducted based on the smart farm technological field. As a result, the education demand survey showed high demand for production systems and intelligent farm machinery. In detail, Borich's analysis showed the need for pest prevention and diagnosis technology (8.03), network and analysis SW linkage technology (7.83), and intelligent farm worker-agricultural power system-electric energy hybrid technology (7.43). In contrast, smart plant factories (4.09), lighting technology for growth control (4.46) and structure construction technology (4.62) showed low demands. Based on this, the IPA portfolio shows that the network and analysis SW linkage technology and the CAN-based complex center are urgently needed. However, the technology that has already been developed, such as smart factory platform development, growth control lighting technology and structure construction technology, was oversized. Based on these results, it is possible to strategically suggest the customized training programs for industrial sectors of SMEs that reflect the needs for efficiently operating smart farms. This study also provides effective ways to operate the relevant training programs.

• Proceedings of the Ginseng society Conference
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• 2004.05a
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• pp.17-28
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• 2004

As Korean ginseng is hybrid, an individual variation is very severe, and it takes long times in new breeding because it is required 4 years to pick the seed. But, transformation technique makes the high-functional breeding in short time. The focus of these ginseng studies is to find and secure the useful gene. And it is urgent to accumulate the fundamental data for the molecular breeding and secure the useful genes. Therefore, transformation and soil acclimatization technique are necessary to molecular breeding in use of the introduction of functional genes. In this study, it add to secure of new regulation gene and useful gene as to accumulate the fundamental data for the place where it will contribute to raise the national competitive power. To analyze the useful genes in large scale, we constructed CDNA libraries with various tissues, species, and treated tissue. EST analysis of ginseng perform in large scale and build the EST database of ginseng. We perform the full length sequencing about the selected lots of clones that include the entire open reading frame of the amino acid residues and construct cDNA chip with the parental EST clones. Establishment of the transformation and a soil acclimatization system throuth the re-introduction of the selected ginseng gene that related with the secondary metabolism and anti-stress into the ginseng.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.145-152
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• 2009

Domestic Kori-1 MCR was partially modified in 2007 and will be renovated entirely in 2013. Digital devices partially replacing original analog devices have been introduced and standard alone computer systems such as SPDS have been integrated into the plant computer. Upgrading KSNP's MCR based on the ditalization is planned for 2015. However, the site engineers and operators are reluctant to the advanced systems. Therefore, a prototype for the KSNP's advanced MCR has been developed to increase the acceptance level of the operators and field engineers and also, to evaluate user interfaces and I&C architecture. For enhancing support of the operators' work, a P&ID based display system composed of multi-layers, which are linked through a context sensitive menu each other, has been adopted. The $1^{st}$ layer displays a simplified P&ID, the $2^{nd}$ layer control related diagrams such as controllers and logic diagrams, the $3^{rd}$ layer trends, etc. The end point view of MCR for KSNP is also suggested considering reliability and operability of the digital systems. Additionally, modernization strategies over the overhaul periods, that do not have much impact on operation and configuration efforts are suggested.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.1-10
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• 2013

RFID(Radio Frequency Identification) system is non-contact identification technology. A basic RFID system consists of a reader, and a set of tags. RFID tags can be divided into active and passive tags. Active tags with power source allows their own operation execution and passive tags are small and low-cost. So passive tags are more suitable for distribution industry than active tags. A reader processes the information receiving from tags. RFID system achieves a fast identification of multiple tags using radio frequency. RFID systems has been applied into a variety of fields such as distribution, logistics, transportation, inventory management, access control, finance and etc. To encourage the introduction of RFID systems, several problems (price, size, power consumption, security) should be resolved. In this paper, we proposed an algorithm to significantly alleviate the collision problem caused by simultaneous responses of multiple tags. In the RFID systems, in anti-collision schemes, there are three methods: probabilistic, deterministic, and hybrid. In this paper, we introduce ALOHA-based protocol as a probabilistic method, and Tree-based protocol as a deterministic one. In Aloha-based protocols, time is divided into multiple slots. Tags randomly select their own IDs and transmit it. But Aloha-based protocol cannot guarantee that all tags are identified because they are probabilistic methods. In contrast, Tree-based protocols guarantee that a reader identifies all tags within the transmission range of the reader. In Tree-based protocols, a reader sends a query, and tags respond it with their own IDs. When a reader sends a query and two or more tags respond, a collision occurs. Then the reader makes and sends a new query. Frequent collisions make the identification performance degrade. Therefore, to identify tags quickly, it is necessary to reduce collisions efficiently. Each RFID tag has an ID of 96bit EPC(Electronic Product Code). The tags in a company or manufacturer have similar tag IDs with the same prefix. Unnecessary collisions occur while identifying multiple tags using Query Tree protocol. It results in growth of query-responses and idle time, which the identification time significantly increases. To solve this problem, Collision Tree protocol and M-ary Query Tree protocol have been proposed. However, in Collision Tree protocol and Query Tree protocol, only one bit is identified during one query-response. And, when similar tag IDs exist, M-ary Query Tree Protocol generates unnecessary query-responses. In this paper, we propose Adaptive M-ary Query Tree protocol that improves the identification performance using m-bit recognition, collision information of tag IDs, and prediction technique. We compare our proposed scheme with other Tree-based protocols under the same conditions. We show that our proposed scheme outperforms others in terms of identification time and identification efficiency.

• Clean Technology
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• v.20 no.4
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• pp.375-382
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• 2014

This work investigated the plasma-catalytic decomposition of isopropyl alcohol (IPA) and the behavior of the byproduct compounds over monolith-supported metal oxide catalysts. Iron oxide ($Fe_2O_3$) or copper oxide (CuO) was loaded on a monolithic porous ${\alpha}-Al_2O_3$ support, which was placed inside the coaxial electrodes of plasma reactor. The IPA decomposition efficiency itself hardly depended on the presence and type of metal oxides because the rate of plasma-induced decomposition was so fast, but the behavior of byproduct formation was largely affected by them. The concentrations of the unwanted byproducts, including acetone, formaldehyde, acetaldehyde, methane, carbon monoxide, etc., were in order of $Fe_2O_3/{\alpha}-Al_2O_3$ < $CuO/{\alpha}-Al_2O_3$ < ${\alpha}-Al_2O_3$ from low to high. Under the condition (flow rate: $1L\;min^{-1}$; IPA concentration: 5,000 ppm; $O_2$ content: 10%; discharge power: 47 W), the selectivity towards $CO_2$ was about 40, 80 and 95% for ${\alpha}-Al_2O_3$, $CuO/{\alpha}-Al_2O_3$ and $Fe_2O_3/{\alpha}-Al_2O_3$, respectively, indicating that $Fe_2O_3/{\alpha}-Al_2O_3$ is the most effective for plasma-catalytic oxidation of IPA. Unlike plasma-alone processes in which tar-like products formed from volatile organic compounds are deposited, the present plasma-catalyst hybrid system did not exhibit such a phenomenon, thus retaining the original catalytic activity.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.