• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.201-207
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• 2012

A RAM-based neural network is a weightless neural network based on binary neural network(BNN) which is efficient neural network with a one-shot learning. RAM-based neural network has multiful information bits and store counts of training in BNN. Supervised learning based on the RAM-based neural network has the excellent performance in pattern recognition but in pattern categorization with unsupervised learning as unsuitable. In this paper, we propose a unsupervised learning algorithm in the RAM-based neural network to perform pattern categorization. By the proposed unsupervised learning algorithm, RAM-based neural network create categories depending on the input pattern by itself. Therefore, RAM-based neural network for supervised learning and unsupervised learning should proof of all possible complex models. The training data for experiments provided by the MNIST offline handwritten digits which is consist of 0 to 9 multi-pattern.

• Proceedings of the Microbiological Society of Korea Conference
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• 2006.05a
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• pp.82-83
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• 2006

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.5_6
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• pp.232-238
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• 2003

Traditional operating systems use a virtual memory to provide users with a bigger memory than a physical memory. The virtual memory augments the insufficient physical memory by the swap device. Since disks are usually used as the swap device, the cost of a page fault is relatively high compared to the access cost of the physical memory. Recently, numerous papers have investigated the Network RAM in order to exploit the idle memory in the network instead of disks. Since today's distributed systems are interconnected with high-performance networks, the network latency is far smaller than the disk access latency In this paper we design and implement the Network RAM using block device driver on Linux. This is the first implementation of the Network RAM on Linux. We propose the new reliability method to recover the page when the other workstation's memory is damaged. The system using the Network RAM as the swap device reduces the execution time by 40.3% than the system using the disk as the swap device. The performance results suggest that the new reliability method that use the processor more efficiently has the similar execution time with others, but uses smaller server memory and generates less message traffic than others.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.44-48
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• 2023

Augmented Reality (AR) is an emerging technology and a vibrant field, it has become common in application development, especially in smartphone applications (mobile phones). The AR technology has grown increasingly during the past decade in many fields. Therefore, it is necessary to determine the optimal approach to building the final product by evaluating the performance of each of them separately at a specific task. In this work we evaluated overall CPU and RAM performance for several types of Markerless Augmented Reality applications by using a multiple-objects in mobile development. The results obtained are show that the objects with fewer number of vertices performs steady and not oscillating. Object was superior to the rest of the others is sphere, which is performs better values when processed, its values closer to the minimum CPU and RAM usage.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.370-388
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• 2015

The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent's position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.12 no.5
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• pp.92-100
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• 2009

This study was surveyed from May to October, 2008 in order to classify type distribution and evaluate the function of inland wetland as a ecological axis in Chungnam province. Assessment was done by modified-RAM (Rapid Assessment Method). RAM is consisted of total 8 functions and divided into high, moderate, low. The conservation grade of RAM is divided into 4 grades; absolute conservation, conservation, improvement and restoration. Throughout survey on total 13 wetlands of Lacustrine, Palustrine wetland which are distributed in Chungnam province, their function was assessed. As result, the 2 wetlands were judged as absolute conservation grade by assessment of 8 functional contents, and 7 sites were improvement wetlands and 4 sites were conservation wetlands. The function of wetlands assessed as conservation grade showed high in water quality protection and improvement. Also, showed high in vegetation diversity, wildlife habitat and aesthetic recreation. Meanwhile, showed low in Water quality purification, Shoreline/Stream Bank Protection. Of wetlands evaluated as conservation grade, Jeong-juk Ji and Dun-ri reservoir were assessed as absolute conservative area. These wetlands are essential to be managed continuously as a area having high ecological value. Farther, these wetlands will be done as a axis of ecological network related to 'Kumbuk jeongmaek' ecosystem.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.31-38
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• 2011

A RAM-based Neural Net is a weightless neural network based on binary neural network. 3-D neural network using this paper is binary neural network with multiful information bits and store counts of training. Recognition method by MRD technique is based on the supervised learning. Therefore neural network by itself can not distinguish between the categories and well-separated categories of training data can achieve only through the performance. In this paper, unsupervised learning algorithm is proposed which is trained existing 3-D neural network without distinction of data, to distinguish between categories depending on the only input training patterns. The training data for proposed unsupervised learning provided by the NIST handwritten digits of MNIST which is consist of 0 to 9 multi-pattern, a randomly materials are used as training patterns. Through experiments, neural network is to determine the number of discriminator which each have an idea of the handwritten digits that can be interpreted.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.6
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• pp.70-80
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• 2007

This study was surveyed from January to september, 2007 in order to evaluate the function of coastal wetland as a ecological axis in korea peninsula. Assessment was done by RAM (Rapid Assessment Method). RAM is consisted of total 8 contents and divided into high, moderate, low. The preservation grade of RAM is divided into 4 grades; absolute preservation, preservation, improvement and improvement or restoration. Throughout survey on total 14 wetlands of marine, estuary wetland and back marsh which are distributed in west coast in chung-nam province, their function was assessed. As result, total all the 14 wetlands were judged as preservation grade by assessment of 8 functional contents. The function of wetlands assessed as preservation grade showed high in water quality protection and improvement. Also, showed high in vegetation diversity, wildlife habitat and aesthetic recreation. Meanwhile, showed low in ground water recharge, Shoreline/Stream Bank Protection, Flood/Stormwater storage and Flood flow alteration. Of wetlands evaluated as preservation grade, Dae-ho, Sinduri, Bu-Nam lake, Sowhang dune and keum river estuary were assessed as absolute preservative area owing to habitation of international protection species and endangered species. These wetlands are essential to be managed continuously as a area having high ecological value. Farther, this wetlands will be done as a axis of ecological network related to land ecosystem.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.95-102
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• 2004

In this paper, Experience Sensitive Cumulative Neural Network (ESCNN) is introduced, which can cumulate the same or similar experiences. As the same or similar training patterns are cumulated in the network, the system recognizes more important information in the training patterns. The functions of forgetting less important information and attending more important information resided in the training patterns are surveyed and implemented by simulations. The system behaves well under the noisy circumstances due to its forgetting and/or attending properties, even in 50 percents noisy environments. This paper also describes the creation of the generalized patterns for the input training patterns.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.4
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• pp.169-175
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• 2014

NG-SDH system is connected with networks throughout optical fibers. Network synchronization controller is a necessary for the data synchronization in each optical transmission system. In this paper, we have design and implementation the network synchronization controller using SOPC(system on a programmable chip) design technic. For this network synchronization controller we use FPGA in Altera. FPGA includes 32bit CPU, DPRAM(dual port ram), digital input/output port, transmitter and receiver framer, phase difference detector. We also confirm that designed network synchronization controller satisfies the ITU-T G.813 timing requirements.