• Smart Structures and Systems
• /
• v.29 no.4
• /
• pp.617-624
• /
• 2022

A new intelligent adaptive control scheme was proposed that combines the control based on interference observer and fuzzy adaptive s-curve for flight path tracking control of unmanned aerial vehicle (UAV). The most important contribution is that the control configurations don't need to know the uncertainty limit of the vehicle and the influence of interference is removed. The proposed control law is an integration of fuzzy control estimator and adaptive proportional integral (PI) compensator with input. The rated feedback drive specifies the desired dynamic properties of the closed control loop based on the known properties of the preferred acceleration vector. At the same time, the adaptive PI control compensate for the unknown of perturbation. Additional terms such as s-surface control can ensure rapid convergence due to the non-linear representation on the surface and also improve the stability. In addition, the observer improves the robustness of the adaptive fuzzy system. It has been proven that the stability of the regulatory system can be ensured according to linear matrix equality based Lyapunov's theory. In summary, the numerical simulation results show the efficiency and the feasibility by the use of the robust control methodology.

• Journal of the Korean Society of Industry Convergence
• /
• v.25 no.6_1
• /
• pp.927-939
• /
• 2022

This study was conducted to develop a Web-GIS based monitoring module of smart city that can effectively respond, manage and improve situation in all stages of illegal dumping management on a city scale. First, five technologies were set for the core technical elements of the module configuration. Five core technical elements are as follows; video screening technology based on motion vector analysis, human behavior detection based on intelligent video analytics technology, mobile app for receiving civil complaints about illegal dumping, illegal dumping risk model and street cleanliness map, Web-GIS based situation monitoring technology. The development contents and results for each set of core technical elements were evaluated. Finally, a Web-GIS based 'illegal dumping monitoring module' was proposed. It is possible to collect and analyze city data at the local government level through operating the proposed module. Based on this, it is able to effectively detect illegal dumpers at relatively low cost and identify the tendency of illegal dumping by systematically managing habitual occurrence areas. In the future, it is expected to be developed in the form of an add-on module of the smart city integration platform operated by local governments to ensure interoperability and scalability.

• Journal of Sensor Science and Technology
• /
• v.31 no.2
• /
• pp.96-101
• /
• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• Geomechanics and Engineering
• /
• v.37 no.6
• /
• pp.539-554
• /
• 2024

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.

• Research in Plant Disease
• /
• v.19 no.4
• /
• pp.254-258
• /
• 2013

Fusaric acid (FA) is a mycotoxin produced by Fusarium species. Its toxicity is relatively low but often associated with other mycotoxins, thus enhancing total toxicity. To date, biosynthetic genes or enzymes for FA have not been identified in F. oxysporum. In order to explore the genetic element(s) for FA biosynthesis, restriction enzyme mediated integration (REMI) procedure as an insertional mutagenesis was employed using FA producing-F. oxysporum strains. Genetic transformation of two F. oxysporum strains by REMI yielded more than 7,100 transformants with efficiency of average 3.2 transformants/${\mu}g$ DNA. To develop a screening system using phytotoxicity of FA, eleven various grains and vegetable seeds were tested for germination in cultures containing FA: Kimchi cabbage seed was selected as the most sensitive host. Screening for FA non-producer of F. oxysporum was done by growing each fungal REMI transformant in Czapek-Dox broth for 3 weeks at $25^{\circ}C$ then observing if the Kimchi cabbage seeds germinated in the culture filtrate. Of more than 5,000 REMI transformants screened, fifty-three made the seeds germinated, indicating that they produced little or fewer FA. Among them, twenty-six were analyzed for FA production by HPLC and two turned out to produce less than 1% of FA produced by a wild type strain. Sequencing of genomic DNA regions (252 bp) flanking the vector insertion site revealed an uncharacterized genomic region homologous (93%) to the F. fujikuroi genome. Further study is necessary to determine if the vector insertion sites in FA-deficient mutants are associated with FA production.

• Journal of Plant Biotechnology
• /
• v.38 no.1
• /
• pp.15-21
• /
• 2011

We developed 14 transgenic lines of Chinese cabbage (Brassica rapa) harboring the T-DNA border sequences and CryIAc1 transgene of the binary vector 416 using Agrobacterium tumefaciens-mediated DNA transfer. Six lines had single copy cryIAc1 gene and four of them contained no vector backbone DNA. Of the left border (LB) flanking sequences six nucleotides were deleted in transgenic lines 416-2 and 416-3, eleven nucleotides in line 416-9, and 65 nucleotides including the whole LB sequences in line 416-17, respectively. And we defined 499 bp of genomic DNA (gDNA) of transformed Chinese cabbage, and blast results showed 96% homology with Brassica oleracea sequences. PCR with specific primer for the right border (RB) franking sequence revealed 834 bp of PCR product sequence, and it was consisted of 3' end of cryIAc1, nosterminal region and 52 bp of Chinese cabbage genomic DNA near RB. RB sequences were not found and the 58 nucleotides including 21 bp of nos-terminator 3' end were deleted. Also, there were deletion of 10 bp of the known genomic sequences and insertion of 65 bp undefined genomic sequences of Chinese cabbage in the integration site. These results demonstrate that the integration of T-DNA can be accompanied by unusual deletions and insertions both in transgenic and genomic sequences.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.221-241
• /
• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Korean Journal of Plant Tissue Culture
• /
• v.24 no.1
• /
• pp.63-69
• /
• 1997

Erythropoietin (EPO) is a glycoprotein that mediates the growth and differentiation of erythroid progenitors. In order to produce recombinant human erythropoietin in tobacco plant, the EPO genomic DNA (5.4 kb) was cloned into plant expression vectors, pBI$\Delta$GUS121, pBD$\Delta$GUS121 and pPEV-1, and introduced in Nicotiana tabacum (var. Xanthi) via Agrobacterium tumefaciens-mediated transformation. After selection on MS media containing kanamycin (Km), 10 Km-resistant plants were obtained per each construct. The correct integration of EPO genomic DNA in the genome of transgenic plant was confirmed by polymerase chain reaction (PCR). Northern blot showed that transcripts of 1.8 kb length were produced in leaves of the plants, but there was no difference of mRNA amount according to promoter number and 5'-untranslated sequence (UTS). The proteins obtained from leaves of transgenic plants were immunologically detected by Western blot using rabbit anti-human EPO polyclonal antibody. The expressed protein appeared as smaller band of apparent mass of 30 kDa as compared to the EPO protein from human urine (37 kDa), suggesting that the modification (glycosylation) system in tobacco plant might be different from that of mammalian cells.

• Journal of Distribution Science
• /
• v.13 no.11
• /
• pp.39-46
• /
• 2015

Purpose - In Korea, there has been a recent trend that shows housing prices have risen rapidly following the International Monetary Fund crisis. The rapid rise in housing prices is spreading recognition of this as a factor in housing price volatility. In addition, this raises the expectations of housing prices in the future. These expectations are based on the assumption that a relationship exists between the current housing prices and expected housing prices in the real estate industry. By performing an empirical analysis on the validity of the claim that an increase in current housing prices can be correlated with expected housing prices, this study examines whether a long-term equilibrium relationship exists between expected housing prices and existing housing prices. If such a relationship exists, the recovery of equilibrium from disequilibrium is analyzed to derive related implications. Research design, data, and methodology - The relationship between current housing prices and expected housing prices was analyzed empirically using the Vector Error Correction Model. This model was applied to the co-integration test, the long-term equilibrium equation among variables, and the causality test. The housing prices used in the analysis were based on the National Housing Price Trend Survey released by Kookmin Bank. Additionally, the Index of Industrial Product and the Consumer Price Index were also used and were obtained from the Bank of Korea ECOS. The monthly data analyzed were from January 1987 to May 2015. Results - First, a long-term equilibrium relationship was established as one co-integration between current housing price distribution and expected housing prices. Second, the sign of the long-term equilibrium relationship variable was consistent with the theoretical sign, with the elasticity of housing price distribution to expected housing price, the industrial production, and the consumer price volatility revealed as 1.600, 0.104,and 0.092, respectively. This implies that the long-term effect of expected housing price volatility on housing price distribution is more significant than that of the industrial production and consumer price volatility. Third, the sign of the coefficient of the error correction term coincided with the theoretical sign. The absolute value of the coefficient of the correction term in the industrial production equation was 0.006, significantly larger than the coefficients for the expected housing price and the consumer price equation. In case of divergence from the long-term equilibrium relationship, the state of equilibrium will be restored through changes in the interest rate. Fourth, housing-price volatility was found to be causal to expected housing price, and was shown to be bi-directionally causal to industrial production. Conclusions - Based on the finding of this study, it is required to relieve the association between current housing price distribution and expected housing price by using property taxes and the loan-to-value policy to stabilize the housing market. Further, the relationship between housing price distribution and expected housing price can be examined and tested using a sophisticated methodology and policy variables.

• Korean Journal of Microbiology
• /
• v.41 no.2
• /
• pp.140-145
• /
• 2005

Streptomyces natalensis ATCC27448 produces natamycin, a commercially important macrolide antifungal antibiotic. For molecular genetic study of S. natalensis, we have developed a system for introducing DNA into S. natalensis via conjugal transfer from Escherichia coli. An effective transformation procedure for S. natalensis was established based on transconjugation from E, coli ET12567/pUZ8002 using a ${\Phi}C31$-derived integration vector, pSET152, containing oriT and attP fragments. The high frequency was obtained on MS medium containing 10 mM $MgCl_2$ using $6.25\times10^8$ of E.coli donor cells without heat treatment of spores. In addition, southern blot analysis of exconjugants and the sequence of plasmids containing DNA flanking the insertion sites from the chromosome revealed that S. natalensis contains a single ${\Phi}C31$ attB site and at least a secondary or pseudo attB site. Similar to the case of various Streptomyces species, a single ${\Phi}C31$ attB site of S. natalensis is present within an ORF encoding a pirin-homolog, but a pseudo-attB site is present within a distinct site (GenBank accession no. $YP\_117731$) and also its sequence deviates from the consensus sequences of attB sequence.