• The Journal of Natural Sciences
• /
• v.8 no.2
• /
• pp.17-20
• /
• 1996

We show Equivariant Serre Problem in real algebraic category is true when dimension of the fiber is one i.e., we show an equivariant line bundle over a real representation space is isomorphic to a product bundle of a real representation space and some G-module.

• The KIPS Transactions:PartC
• /
• v.14C no.7
• /
• pp.553-558
• /
• 2007

In this paper, we introduce a set of structures and algorithms for communication efficient public key revocation in wireless sensor networks. Unlike the traditional networks, wireless sensor network is subjected to resources constraints. Thus, traditional public key revocation mechanisms such like the ordinary certificate revocation list is unsuitable to be used. This unsuitability is due to the huge size of required representation space for the different keys' identifiers and the revocation communication as the set of revoked keys grow. In this work, we introduce two communication-efficient schemes for the certificate revocation. In the first scheme, we utilize the complete subtree mechanism for the identifiers representation which is widely used in the broadcast encryption/user revocation. In the second scheme, we introduce a novel bit vector representation BVS which uses vector of relative identifiers occurrence representation. We introduce different revocation policies and present corresponding modifications of our scheme. Finally, we show how the encoding could reduce the communication overhead as well. Simulation results and comparisons are provided to show the value of our work.

• East Asian mathematical journal
• /
• v.11 no.2
• /
• pp.229-235
• /
• 1995

• Proceedings of the KIEE Conference
• /
• 1990.11a
• /
• pp.450-456
• /
• 1990

In this paper, production system is implemented with the inferential neural network model using semantic network and directed graph. Production system can be implemented with the transform of knowledge representation in production system into semantic network and of semantic network into directed graph, because directed graphs can be expressed by neural matrices. A concept node should be defined by the state vector to calculated the concepts expressed by matrices. The expressional ability of neunal network depends on how the state vector is defined. In this study, state vector is overlapped and each overlapping part acts as a inheritant of concept.

• Journal of the Korean Mathematical Society
• /
• v.54 no.1
• /
• pp.227-248
• /
• 2017

In this paper, we reduce the classification problem of equivariant (topological complex) vector bundles over a simple graph to the classification problem of their isotropy representations at vertices and midpoints of edges. Then, we solve the reduced problem in the case when the simple graph is homeomorphic to a circle. So, the paper could be considered as a generalization of [3].

• Bulletin of the Korean Mathematical Society
• /
• v.58 no.2
• /
• pp.507-514
• /
• 2021

Vector-valued Eisenstein series of weight 3/2 are often not holomorphic. In this paper we prove that, for an even lattice Ḻ, if there exists an odd prime p such that Ḻ is local p-maximal and the determinant of Ḻ is divisible by p2, then the Eisenstein series of weight 3/2 attached to the discriminant form of Ḻ is holomorphic.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.11 no.4
• /
• pp.238-246
• /
• 2011

This paper proposes a new semantic representation and its associated similarity measure. The representation expresses textual context observed in a context of a certain term as a network where nodes are terms and edges are the number of cooccurrences between connected terms. To compare terms represented in networks, a graph kernel is adopted as a similarity measure. The proposed representation has two notable merits compared with previous semantic representations. First, it can process polysemous words in a better way than a vector representation. A network of a polysemous term is regarded as a combination of sub-networks that represent senses and the appropriate sub-network is identified by context before compared by the kernel. Second, the representation permits not only words but also senses or contexts to be represented directly from corresponding set of terms. The validity of the representation and its similarity measure is evaluated with two tasks: synonym test and unsupervised word sense disambiguation. The method performed well and could compete with the state-of-the-art unsupervised methods.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.22 no.8
• /
• pp.777-783
• /
• 2012

Vibration intensity has been used to identify the location of a vibration source in a vibrating system. By using vectors representation, the source of the power flow and the vibration energy transmission paths can be revealed. However, due to the large surface area of a plate-like structure, clear transmission paths cannot be achieved using the vectors representation. Experimentally, for a large surface object, the number of measured points will also be increased. This requires a lot of time for measurement. In this study, streamlines representation is used to clearly indicate the power flow transmission paths at all surface plate for FEM and experiment. To clearly improve the vibration intensity transmission paths, streamlines representation from experimental works and FEM computations are compared. Improved transmission paths visualization for both FEM and experiment are shown in comparison to conventional vectors representation. These streamlines visualization is useful to clearly identify vibration source and detail energy transmission paths especially for large surface plate-like structures. Not only that, this visualization does not need many measured point either for experiment or FEM analysis.

• Journal of Information Processing Systems
• /
• v.16 no.2
• /
• pp.343-359
• /
• 2020

In this paper, a new algorithm is proposed for three-dimensional (3D) shape recognition using local features of model views and its sparse representation. The algorithm starts with the normalization of 3D models and the extraction of 2D views from uniformly distributed viewpoints. Consequently, the 2D views are stacked over each other to from view cubes. The algorithm employs the descriptors of 3D local features in the view cubes after applying Gabor filters in various directions as the initial features for 3D shape recognition. In the training stage, we store some 3D local features to build the prototype dictionary of local features. To extract an intermediate feature vector, we measure the similarity between the local descriptors of a shape model and the local features of the prototype dictionary. We represent the intermediate feature vectors of 3D models in the sparse domain to obtain the final descriptors of the models. Finally, support vector machine classifiers are used to recognize the 3D models. Experimental results using the Princeton Shape Benchmark database showed the average recognition rate of 89.7% using 20 views. We compared the proposed approach with state-of-the-art approaches and the results showed the effectiveness of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.9
• /
• pp.817-821
• /
• 2001

In this paper, a new learning methodology for kernel methods that results in a sparse representation of kernel space from the training patterns for classification problems is suggested. Among the traditional algorithms of linear discriminant function, this paper shows that the relaxation procedure can obtain the maximum margin separating hyperplane of linearly separable pattern classification problem as SVM(Support Vector Machine) classifier does. The original relaxation method gives only the necessary condition of SV patterns. We suggest the sufficient condition to identify the SV patterns in the learning epoches. For sequential learning of kernel methods, extended SVM and kernel discriminant function are defined. Systematic derivation of learning algorithm is introduced. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.