• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.496-499
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• 1999

This paper describes a medular neural network(MNN) for a vision system which tracks a given object using a sequence of images from a camera unit. The MNN is used to precisely recognize the given robot endeffector and to minize the processing time. Since the robot endeffector can be viewed in many different shapes in 3-D space, a MNN structure, which contains a set of feedforwared neural networks, co be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training patterns for a neural network share the similar charateristics so that they can be easily trained. The trained MNN is less sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14% over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• Convergence Security Journal
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• v.16 no.5
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• pp.41-48
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• 2016

In this paper we propose a new method of security visualization, VisFlow, using traffic flows to solve the problems of existing traffic flows based visualization techniques that were a loss of end-to-end semantics of communication, reflection problem by symmetrical address coordinates space, and intuitive loss problem in mass of traffic. VisFlow, a simple and effective security visualization interface, can do a real-time analysis and monitoring the situation in the managed network with visualizing a variety of network behavior not seen in the individual traffic data that can be shaped into patterns. This is a way to increase the intuitiveness and usability by identifying the role of nodes and by visualizing the highlighted or simplified information based on their importance in 2D/3D space. In addition, it monitor the network security situation as a way to increase the informational effectively using the asymmetrical connecting line based on IP addresses between pairs of nodes. Administrator can do a real-time analysis and monitoring the situation in the managed network using VisFlow, it makes to effectively investigate the massive traffic data and is easy to intuitively understand the entire network situation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.61-80
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• 2018

Location information of individual nodes is important in the implementation of necessary network functions. While extensive studies focus on localization techniques in 2D space, few approaches have been proposed for 3D positioning, which brings the location closer to the reality with more complex calculation consumptions for high accuracy. In this paper, an effective range-free localization scheme is proposed for 3D space localization, and the sensitivity of parameters is evaluated. Firstly, we present an improved algorithm (MDV-Hop), that the average distance per hop of the anchor nodes is calculated by root-mean-square error (RMSE), and is dynamically corrected in groups with the weighted RMSE based on group hops. For more improvement in accuracy, we expand particle swarm optimization (PSO) of intelligent optimization algorithms to MDV-Hop localization algorithm, called PMDV-hop, in which the parameters (inertia weight and trust coefficient) in PSO are calculated dynamically. Secondly, the effect of various localization parameters affecting the PMDV-hop performance is also present. The simulation results show that PMDV-hop performs better in positioning accuracy with limited energy.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.37-42
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• 2022

Communication as a process of interpreting signs has always existed in people's life. In post-information society, the communication between a person and a technological system through the specific sign-attaching process becomes widespread. Moreover, it somehow replaces usual communication between people. One of the means of communication in the digital space are video games. They not only play an important role in communication processes, but also are a special case of sign-creating and interpreting. The purpose of the article is to examine video games as a space of sign-based communication between a person and a game as a specific digital system. With the help of general scientific and hermeneutic methodology, the analysis of video games as a post-information society phenomenon which people communicate to has been conducted. The process of semiosis as attaching special meanings to signs has been traced in both manipulating in-game objects and characters and understanding rules of an in-game world.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.193-198
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• 2023

The article focuses on the areas of education activities in using techniques for teaching and learning with information and communication technologies (ICTs), researching and analyzing the available ICTs, gearing the technologies to the specific psychological and pedagogical conditions, independently building and modeling ICTs, enlarging and developing their use in the learning environment. The visualization of scientific research has been determined to be part of the educational support for building students' ICT competence during teaching and learning and is essential to the methodology culture. There have been specified main tasks for pedagogy technologies (PTs) to develop the skills of adaptability to the global digital space in students, their effective database operation and using the data bases as necessary elements for learning and as part of professional training for research. We provided rationalization for implementing the latest ICTs into the Ukrainian universities' curricula, as well as creating modern methods for using the technologies in the learning / teaching process and scientific activities.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.79-88
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• 2024

The information retrieval domain deals with the retrieval of unstructured data such as text documents. Searching documents is a main component of the modern information retrieval system. Locality Sensitive Hashing (LSH) is one of the most popular methods used in searching for documents in a high-dimensional space. The main benefit of LSH is its theoretical guarantee of query accuracy in a multi-dimensional space. More enhancement can be achieved to LSH by adding a bit to its steps. In this paper, a new Dynamic Locality Sensitive Hashing (DLSH) algorithm is proposed as an improved version of the LSH algorithm, which relies on employing the hierarchal selection of LSH parameters (number of bands, number of shingles, and number of permutation lists) based on the similarity achieved by the algorithm to optimize searching accuracy and increasing its score. Using several tampered file structures, the technique was applied, and the performance is evaluated. In some circumstances, the accuracy of matching with DLSH exceeds 95% with the optimal parameter value selected for the number of bands, the number of shingles, and the number of permutations lists of the DLSH algorithm. The result makes DLSH algorithm suitable to be applied in many critical applications that depend on accurate searching such as forensics technology.

• Smart Media Journal
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• v.5 no.3
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• pp.54-61
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• 2016

According to many smart devices are development, SNS(Social Network Service) users are getting higher that is possible for real-time communicating, information sharing without limitations in distance and space. Nowadays, SNS users that based on communication and relationships, are getting uses SNS for information sharing. In this paper, we used the SNS posts for users to extract the category and information provider, how to following of recommend method. Particularly, this paper focuses on classifying the words in the text of the posts and measures the frequency using Inception-v3 model, which is one of the machine learning technique -CNN(Convolutional Neural Network) we classified image word. By classifying the category of a word in a text and image, that based on DMOZ to build the information provider DB. Comparing user categories classified in categories and posts from information provider DB. If the category is matched by measuring the degree of similarity to the information providers is classified in the category, we suggest that how to recommend method of the most similar information providers account.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.30-33
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• 2001

The number of hidden neurons of the feed-forward neural networks is generally decided on the basis of experience. The method usually results in the lack or redundancy of hidden neurons, and causes the shortage of capacity for storing information of learning overmuch. This research proposes a new method for optimizing the number of hidden neurons bases on information entropy, Firstly, an initial neural network with enough hidden neurons should be trained by a set of training samples. Second, the activation values of hidden neurons should be calculated by inputting the training samples that can be identified correctly by the trained neural network. Third, all kinds of partitions should be tried and its information gain should be calculated, and then a decision-tree correctly dividing the whole sample space can be constructed. Finally, the important and related hidden neurons that are included in the tree can be found by searching the whole tree, and other redundant hidden neurons can be deleted. Thus, the number of hidden neurons can be decided. In the case of building a neural network with the best number of hidden units for tea quality evaluation, the proposed method is applied. And the result shows that the method is effective

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.10
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• pp.20-28
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• 2007

Zigbee sensor network base on IEEE802.15.4 has local address of 2 byte on transmit packet data which is pick up the address for each sensor node. Sensor network is requested low power, low cost, many nodes at hues physical area. There for Zigbee is very good solution supporting for next Ubiquitous generation but the Zigbee sensor network has address allocation problem of each sensor node. Is established standard from Zigbee Alliance, to the address allocation method uses Cskip algorithm. The Cskip algorithm use the hazard which allocates an address must blow Hop of the maximum modification and child node number. There is to address allocation and from theoretically it will be able to compose a personal 65536 sensor nodes only actual with concept or space, only 500 degree will be able to compose expansion or the low Zigbee network. We proposed an address allocation method using coordinate value for Zigbee sensor network.