• Journal of the Korean Ceramic Society
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• v.41 no.4
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• pp.323-327
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• 2004

This study has been carried out to use the municipal solid waste sewage sludge ash generated at sewage disposal field as the raw materials of Ordinary Portland Cement (OPC). Limestone, shale, converter slag, and fly ash were used as the main raw materials. After the raw materials were mixed, these were fired at 1,300, 1,350, 1,400, 1,450, and 1,500$^{\circ}C$ for 1 h and cooled rapidly in air. The properties of clinker synthesized were examined with XRD, SEM, and burnability index by polysius method.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.411-414
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• 2021

One of the elements of technology that has become extremely critical within the field of education today is Deep learning. It has been especially used in the area of natural language processing, with some word-representation vectors playing a critical role. However, some of the low-resource languages, such as Swahili, which is spoken in East and Central Africa, do not fall into this category. Natural Language Processing is a field of artificial intelligence where systems and computational algorithms are built that can automatically understand, analyze, manipulate, and potentially generate human language. After coming to discover that some African languages fail to have a proper representation within language processing, even going so far as to describe them as lower resource languages because of inadequate data for NLP, we decided to study the Swahili language. As it stands currently, language modeling using neural networks requires adequate data to guarantee quality word representation, which is important for natural language processing (NLP) tasks. Most African languages have no data for such processing. The main aim of this project is to recognize and focus on the classification of words in English, Swahili, and Korean with a particular emphasis on the low-resource Swahili language. Finally, we are going to create our own dataset and reprocess the data using Python Script, formulate the syllabic alphabet, and finally develop an English, Swahili, and Korean word analogy dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.146-148
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• 2014

In LTE-Advanced (LTE-A) networks, Relay nodes (RN) are used to improve the system coverage. However, it also brings new kind of interference to users which reduces the system performance. In this paper, we use an optimization relay node deployment to reduce the interference as much as possible and resource allocation to improve the user throughput. Our simulation results show our method is able to improve the user SINR and throughput.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1113-1116
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• 2006

Security in wireless sensor networks is very pressing especially when sensor nodes are deployed in hostile environments. To obtain security purposes, it is essential to be able to encrypt and authenticate messages sent amongst sensor nodes. Keys for encryption and authentication must be agreed upon by communicating nodes. Due to resource limitations and other unique features, obtaining such key agreement in wireless sensor network is extremely complex. Many key agreement schemes used in general networks, such as trusted server, Diffie-Hellman and public-key based schemes, are not suitable for wireless sensor networks [1], [2], [5], [7], [8]. In that situation, key pre-distribution scheme has been emerged and considered as the most appropriate scheme [2], [5], [7]. Based on that sense, we propose a new resource-optimal key pre-distribution scheme utilizing merits of the two existing key pre-distribution schemes [3], [4]. Our scheme exhibits the fascinating properties: substantial improvement in sensors' resource usage, rigorous guarantee of successfully deriving pairwise keys between any pair of nodes, greatly improved network resiliency against node capture attack. We also present a detailed analysis in terms of security and resource usage of the scheme.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1480-1493
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• 2018

Recently, computational intelligence has received a lot of attention from researchers due to its potential applications to artificial intelligence. In computer science, computational intelligence refers to a machine's ability to learn how to compete various tasks, such as making observations or carrying out experiments. We adopted a computational intelligence solution to monitoring residual resources in cloud computing environments. The proposed residual resource monitoring scheme periodically monitors the cloud-based host machines, so that the post migration performance of a virtual machine is as consistent with the pre-migration performance as possible. To this end, we use a novel similarity measure to find the best target host to migrate a virtual machine to. The design of the proposed residual resource monitoring scheme helps maintain the quality of service and service level agreement during the migration. We carried out a number of experimental evaluations to demonstrate the effectiveness of the proposed residual resource monitoring scheme. Our results show that the proposed scheme intelligently measures the similarities between virtual machines in cloud computing environments without causing performance degradation, whilst preserving the quality of service and service level agreement.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1100-1110
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• 2003

Design and implementation of remote procedure cail (RPC) resource service system based on the XML considering the request delegation between the servers are described for the resource application and efficient processing of distributed environment. For this purpose, the request information about several server, which would be used by client, is encoded into XML document based on XML-DOM and transferred to server. Server classifies the client requests using objects which can deal with the XMLL-DOM. For the request delegation between the servers, server saves the request result of client in XMLL-DOM structure without replying it immediately, and makes the client request delegated to another server in XML-DOM's request information. In addition, the resource repository are managed for resource transparency of distributed environment. As a result, resource application and processing efficiency are improved by reduction of response time caused by integration of RPC and XML-RPC services and request delegation between the servers. Extension of asynchronous and average blocking time of client were reduced and information of resource which can be used by client was offered.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.127-141
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• 2014

A Vehicle Tracking System consists of GPS tracking device which fits into the vehicle and captures the GPS location information at regular intervals to a central GIS server, and GIS tracking server providing three major responsibilities: receiving data from the GPS tracking unit, securely storing it, and serving this information on demand of the user. GPS based tracking systems supporting a multi-session processing among RMA, RM, and RCP can make a quick response to various services including other vehicle information between RSU and OBU on demand of the user. In this paper we design RSU lower layers and RCP applications in OBU for a multisession processing simulation and test message processing transactions among RMA-RM and RM-RCP. Furthermore, we implement the additional functions of handling access commands simultaneously on multiple service resources which are appropriate for the experimental testing conditions. In order to make a multi-session processing test, it reads 30 resource data,0002/0001 ~ 0002/0030, in total and then occurs 30 session data transmissions simultaneously. We insert a sequence number field into a special header of dummy data as a corresponding response to check that the messages are received correctly. Thus, we find that GIS service system with a multi-session processing is able to provide additional 30 services in a same speed of screen presentation loading while identifying the number of session processing of Web GIS service, the number of OBU service, and the speed of screen presentation loading by comparing a single session and a multi-session of GIS service system.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.9
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• pp.1337-1344
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• 2000

Differences in the physical structure and chemical composition of sorghums result in different nutritional values. Sorghums with high in vitro nutrient digestibility tend to have greater ileal and total tract nutrient digestibilies. Soft endosperm can improve growth and nutrient digestibility in nursery pigs and broiler chicks. However, finishing pigs respond less to endosperm hardness. Chicks benefit from waxy sorghums, but responses of swine to waxy sorghum remain controversial. Reduction of particle size benefits nursery pigs more than finishing pigs, while age of chicks affects the coarseness preference. Nutritional benefits of thermal processing in sorghum remain unclear in chicks and pigs. Although experiments have demonstrated increased efficiency with processed sorghum, processing provided only an immediate solution to the problem of reduced utilization. Long-term, solutions will be genetic improvement of physical and on chemical characteristic.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.111-119
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• 2014

The cloud environment need resource management method that to enable the big data issue and data analysis technology. Existing resource management uses the limited calculation method, therefore concentrated the resource bias problem. To solve this problem, the resource management requires the learning-based scheduling using resource history information. In this paper, we proposes the ART (Adaptive Resonance Theory)-based adaptive resource management. Our proposed method assigns the job to the suitable method with the resource monitoring and history management in cloud computing environment. The proposed method utilizes the unsupervised learning method. Our goal is to improve the data processing and service stability with the adaptive resource management. The propose method allow the systematic management, and utilize the available resource efficiently.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.449-462
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• 2014

In big data era, there are a number of considerable parts in processing systems for capturing, storing, and analyzing stored or streaming data. Unlike traditional data handling systems, a big data processing system needs to concern the characteristics (format, velocity, and volume) of being handled data in the system. In this situation, virtualized computing platform is an emerging platform for handling big data effectively, since virtualization technology enables to manage computing resources dynamically and elastically with minimum efforts. In this paper, we analyze resource utilization of virtualized computing resources to discover suitable deployment models in Apache Hadoop and HBase-based big data processing environment. Consequently, Task Tracker service shows high CPU utilization and high Disk I/O overhead during MapReduce phases. Moreover, HRegion service indicates high network resource consumption for transfer the traffic data from DataNode to Task Tracker. DataNode shows high memory resource utilization and Disk I/O overhead for reading stored data.