• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.535-546
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• 2011

This study develops statistical models for the binary forecast of Asian dust days over South Korea in the winter season. For this study, we used three kinds of data; the rst one is the observed Asian dust days for a period of 31 years (1980 to 2010) as target values, the second one is four meteorological factors(near surface temperature, precipitation, snowfall, ground wind speed) in the source regions of Asian dust based on the NCEP reanalysis data and the third one is the large-scale climate indices. Four kinds of statistical models(multiple regression models, logistic regression models, decision trees, and support vector machines) are applied and compared based on skill scores(hit rate, probability of detection and false alarm rate).

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.5
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• pp.76-85
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• 1999

The intuitive understanding of the dynamic pattern generation in asymmetric networks may be useful for developing models of dynamic information processing. In this paper, dynamic behavior of the cyclic connection neural network, in which each neuron is connected only to its nearest neurons with binary synaptic weights of $\pm$ 1, has been investigated. Simulation results show that dynamic behavior of the network can be classified into only three categories: fixed points, limit cycles with basin and limit cycles with no basin. Furthermore, the number and the type of limit cycles generated by the networks have been derived through analytical method. The sufficient conditions for a state vector of $n$-neuron network to produce a limit cycle of $n$- or 2$n$-period are also given. The results show that the estimated number of limit cycles is an exponential function of $n$. On the basis of this study, cyclic connection neural network may be capable of storing a large number of dynamic information.

• Korean Journal of Plant Tissue Culture
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• v.27 no.1
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• pp.39-45
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• 2000

Two maize transposable elements, immobilized Ac (iAc) and Ds, have been introduced into the genome of a diploid potato clone (Solanum tuberosum Group Phureja clone 1.22). The iAc is a modified Ac that is supposed to be unable to transpose but is expected to trans-activate the transposition of a Ds that is unable to transpose by itself. When the leaf and stem explants of in vitro shoots of the clone 1.22 were inoculated with Agrobacterium tumefaciens strains harboring binary vectors containing the iAc and the Ds, calli were formed from the explants on media containing 50 mg/L of kanamycin, and shoots were regenerated from the calli. The regenerated shoots formed roots when cultured on media containing 100 mg/L of kanamycin, whereas untransformed shoots did not form roots on the same media. The PCR amplification of the DNA's from the transgenic plants confirmed that the iAc and the Ds elements were introduced into the potato genome of 1.22.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3312-3327
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• 2020

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.149-156
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• 2003

This paper proposes a face recognition method based on modified Otsu binarization, Hu moment and linear discriminant analysis (LDA). Proposed method is robust to brightness, contrast, scale, rotation, and translation changes. Modified Otsu binarization can make binary images that have the invariant characteristic in brightness and contrast changes. From edge and multi-level binary images obtained by the threshold method, we compute the 17 dimensional Hu moment and then extract feature vector using LDA algorithm. Especially, our face recognition system is robust to scale, rotation, and translation changes because of using Hu moment. Experimental results showed that our method had almost a superior performance compared with the conventional well-known principal component analysis (PCA) and the method combined PCA and LDA in the perspective of brightness, contrast, scale, rotation, and translation changes with Olivetti Research Laboratory (ORL) database and the AR database.

• Korean Journal of Plant Tissue Culture
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• v.25 no.5
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• pp.329-333
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• 1998

$\beta$-Glucuronidase (GUS) gene of Escherichia coli was introduced into sweet potato (Ipomoea batatas (L.) Lam.) cells by particle bombardment and expressed in the regenerated plants. Microprojectiles coated with DNA of a binary vector pBI121 carrying CaMV35S promoter-GUS gene fusion and a neomycin phosphotransferase gene as selection marker were bombarded on embryogenic calli which originated from shoot apical meristem-derived callus and transferred to Murashige and Skoog (MS) medium supplemented with 1 mg/L 2,4-dichlorophenoxyacetic acid and 100 mg/L kanamycin. Bombarded calli were subcultured at 4 week intervals for six months. Kanamycin-resistant calli transferred to MS medium supplemented with 0.03 mg/L 2iP, 0.03 mg/L ABA, and 50 mg/L kanamycin gave rise to somatic embryos. Upon transfer to MS basal medium without kanamycin, they developed into plantlets. PCR and northern analyses of six regenerants transplanted to potting soil confirmed that the GUS gene was inserted into the genome of the six regenerated plants. A histochemical assay revealed that the GUS gene was preferentially expressed in the vascular bundle and the epidermal layer of leaf, petiole, and tuberous root.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.136-142
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• 2007

Recent researches in the recognition system have shown that SVM (Support Vector Machine) classifiers often have superior recognition rates in comparison to other classifiers. In this paper, we present the handwritten numeral recognition algorithm using SVM classifiers. The numeral features used in our algorithm are mesh features, directional features by Kirsch operators and concavity features, where first two features represent the foreground information of numerals and the last feature represents the background information of numerals. These features are complements each of the other. Since SVM is basically a binary classifier, it is required to construct and combine several binary SVMs to get the multi-class classifiers. We use two strategies for implementing multi-class SVM classifiers: "one against one" and "one against the rest", and examine their performances on the features used. The efficiency of our method is tested by the CENPARMI handwritten numeral database, and the recognition rate of 98.45% is achieved.

• Korean Journal of Plant Tissue Culture
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• v.21 no.5
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• pp.315-318
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• 1994

To obtain transformed plants, we cocultured cotyledonary explants of Codonopsis lanceolata with Agrobacterium tumefaciens LBA4404, a disamed strain harboring a binary vector pBI121 carrying the CaMV35S promoter-$\beta$-glucuronidase (GUS) gene fusion used as a reporter gene and NOS promoter-neomycin phosphotransferase gene as a positive selection marker in MS liquid medium with 1mg/L BA. After 48 h of culture, explants were transferred onto MS solid medium with Img/L BA, 250mg/L carbenicillin, and 100mg/L kanamycin sulfate and cultured in the dark. Numerous adventitious buds formed on the cut edges of the explants after 2 weeks of culture. When subjected to GUS histochemical assay buds showed a positive response at a frequency of 15%. Explants formed adventitious shoot at a frequency of 56.7%, after 6 weeks of culture. Upon transfer onto the basal medium, most of the shoots were rooted and subsequently the regenerants were transplanted to potting soil. Southern blot analysis confirmed that the GUS gene was incorporated into the genomic DNA of the GUS-positive regenerants.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.97-107
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• 2006

In this paper, we present a method for the detection and recognition of rectangular markers from a camera image. It converts the camera image to a binary image and extracts contours of objects in the binary image. After that. it approximates the contours to a list of line segments. It finds rectangular markers by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted markers into exact squares by using the warping technique. It extracts feature vectors from marker image by using principal component analysis. It then calculates the distance between feature vector of input marker image and those of standard markers. Finally, it recognizes the marker by using minimum distance method. Experimental results show that the Proposed method achieves 98% recognition rate at maximum for 50 markers and execution speed of 11.1 frames/sec for images which contains eleven markers.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.