• Kyungpook Mathematical Journal
• /
• v.59 no.3
• /
• pp.403-414
• /
• 2019

Theta functions are the sections of line bundles on a complex torus. Noncommutative versions of theta functions have appeared as theta vectors and quantum theta operators. In this paper we describe a super version of theta vectors and quantum theta operators. This is the natural unification of Manin's result on bosonic operators, and the author's previous result on fermionic operators.

• Journal of the Korean Geophysical Society
• /
• v.9 no.2
• /
• pp.99-103
• /
• 2006

3D object recognition independent of translation and rotation using an ultrasonic sensor array, invariant moment vectors and SOFM(Self Organizing Feature Map) neural networks is presented. Using invariant moment vectors of the acquired 16×8 pixel data of square, rectangular, cylindric and regular triangular blocks, 3D objects could be classified by SOFM neural networks. Invariant moment vectors are constant independent of translation and rotation. The recognition rates for the training and testing data were 95.91% and 92.13%, respectively.

• Korean Journal of Mathematics
• /
• v.32 no.2
• /
• pp.259-284
• /
• 2024

In present study, we introduce ruled surfaces whose base curve is the Salkowski curve in Euclidean 3-space and whose generating lines consist of the Frenet vectors of this curve (tangent, principal normal and binormal vectors). Then, we produce regular surfaces from a vector with real coefficients, which is a linear combination of these vectors, and we examine some special cases for these surfaces. Moreover, we present some geometric properties and graphics of all these surfaces.

• Parasites, Hosts and Diseases
• /
• v.61 no.3
• /
• pp.272-281
• /
• 2023

Chigger mites are the vector of scrub typhus. This study estimates the infestation status and ecological characteristics of chiggers on the chestnut white-bellied rat Niviventer fulvescens in Southwest China between 2001 and 2019. Chiggers were identified under the microscope, and infestation indices were calculated. The Preston's log-normal model was used to fit the curve of species abundance distribution. A total of 6,557 chiggers were collected in 136 of 342 N. fulvescens rats, showing high overall infestation indices (prevalence=39.8%, mean abundance=19.2, mean intensity=48.2) and high species diversity (S=100, H'=3.0). Leptotrombidium cangjiangense, Neotrombicula japonica, and Ascoschoengastia sifanga were the three dominant chigger species (constituent ratio=42.9%; 2,736/6,384) and exhibited an aggregated distribution among different rat individuals. We identified 100 chigger species, with 3 of them (Leptotrombidium scutellare, Leptotrombidium wenense, and Leptotrombidium deliense) as the main vectors of scrub typhus in China and nine species as potential vectors of this disease. Disease vector occurrence on N. fulvescens may increase the risk of spreading scrub typhus from rats to humans. Chigger infestation on N. fulvescens varied significantly in different environments. The species abundance distribution showed a log-normal distribution pattern. The estimated number of chigger species on N. fulvescens was 126 species.

• Journal of radiological science and technology
• /
• v.30 no.4
• /
• pp.419-426
• /
• 2007

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize taste(bitter, sweet, sour and salty) pattern vectors. The signal intensity of taste are used to compose the input pattern vectors. The SOM(Self Organizing Maps) algorithm for taste pattern recognition is used to learn initial reference vectors and the ot-star learning algorithm is used to determine the class of the output neurons of the sunclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ(Learning Vector Quantization) algorithm. The pattern vectors are classified into subclasses by neurons in the subclass layer, and the weights between subclass layer and output layer are learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors, the proposed algorithm is simulated with ones of the conventional LVQ, and it is confirmed that the proposed learning method is more successful classification than the conventional LVQ.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.4
• /
• pp.586-591
• /
• 2009

In this paper, we propose the mobile object tracking algorithm based on the feature vector using particle filter. To do this, first, we detect the movement area of mobile object by using RGB color model and extract the feature vectors of the input image by using the KLT-algorithm. And then, we get the first feature vectors by matching extracted feature vectors to the detected movement area. Second, we detect new movement area of the mobile objects by using RGB and HSI color model, and get the new feature vectors by applying the new feature vectors to the snake algorithm. And then, we find the second feature vectors by applying the second feature vectors to new movement area. So, we design the mobile object tracking algorithm by applying the second feature vectors to particle filter. Finally, we validate the applicability of the proposed method through the experience in a complex environment.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.5 s.43
• /
• pp.95-103
• /
• 2006

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize EEG pattern vectors. The frequency and amplitude of alpha rhythms and beta rhythms are used to compose the input pattern vectors. And the algorithm for EEG pattern recognition is used SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of the subclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights between subclass layer and output layer is learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors of EEG, the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

• Proceedings of the KAIS Fall Conference
• /
• 2003.11a
• /
• pp.223-230
• /
• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.

• Parasites, Hosts and Diseases
• /
• v.59 no.4
• /
• pp.377-392
• /
• 2021

This paper is to illustrate the infestation and related ecological characteristics of chigger mites on the Asian house rat (Rattus tanezumi). A total of 17,221 chigger mites were collected from 2,761 R. tanezumi rats, and then identified as 131 species and 19 genera in 2 families. Leptotrombidium deliense, the most powerful vector of scrub typhus in China, was the first major dominant species on R. tanezumi. All the dominant mite species were of an aggregated distribution among different individuals of R. tanezumi. The species composition and infestations of chiggers on R. tanezumi varied along different geographical regions, habitats and altitudes. The species-abundance distribution of the chigger mite community was successfully fitted and the theoretical curve equation was ${\hat{S}}(R)={37e^{-(0.28R)}}^2$. The total chigger species on R. tanezumi were estimated to be 199 species or 234 species, and this further suggested that R. tanezumi has a great potential to harbor abundant species of chigger mites. The results of the species-plot relationship indicated that the chig-ger mite community on R. tanezumi in Yunnan was an uneven community with very high heterogeneity. Wide geographi-cal regions with large host samples are recommended in the investigations of chigger mites.

• Journal of applied mathematics & informatics
• /
• v.11 no.1_2
• /
• pp.437-444
• /
• 2003

A toric variety is defined by a certain collection of cones. Especially a toric Fano variety is obtained from a special nonsingular fan. In this paper, we define the f-vectors of toric Fano varieties as the numbers of faces of the corresponding fans, and investigate the f-vectors of some toric Fano varieties.