• IE interfaces
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• v.13 no.3
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• pp.528-538
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• 2000

HLA is a general-purpose software architecture for distributed simulation designed to support a wide range of simulation approaches and application. The US DoD's HLA for modeling and simulation can certainly be regarded as the state of the art in distributed simulation. It is a mandatory standard for military simulation. The purpose of this paper is to describe applications of HLA/RTI in multiple domains across the manufacturing systems society. In many and large scale industrial systems, enormous data is generated, and is to be managed in an effective way. It needs a high performance common network library. Furthermore, it must satisfy the real function of system facilities as much as possible. The RTI is an implementation of the interface specification, provided as a set of services. Some applications focusing on the area of a small manufacturing system were demonstrated. The integration could be achieved using the HLA, together with interface modules for each of the subsystems. We have found that HLA/RTI are cable of meeting the functional requirements for a given system environment.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.11
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• pp.132-139
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• 1993

This paper presents a new performance-driven multi-levelizer which transforms a two-level description into a boolean network of the multilevel structure satisfied with user's costraints, such as chip area, the number of wires and literals, maximum delay, function level, fanin, fanout, etc.. The performance of circuits is estimated by reference to the informations in cell library through the cell mapping phase, and multi-levelization of circuits is constructed by the decomposition using the kernel and factoring concepts. Here, the saving cost of a common subexpression is defined to the sum of area and delay saved, when it is substituted. The experiments with MCNC benchmarks show the efficiency of the proposed method.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.269-274
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• 2022

An artificial neural network (ANN) that identifies radionuclides from low-count gamma spectra of a NaI scintillator is proposed. The ANN was trained and tested using simulated spectra. 14 target nuclides were considered corresponding to the requisite radionuclide library of a radionuclide identification device mentioned in IEC 62327-2017. The network shows an average identification accuracy of 98.63% on the validation dataset, with the gross counts in each spectrum N_c = 100~10000 and the signal to noise ratio SNR = 0.05-1. Most of the false predictions come from nuclides with low branching ratio and/or similar decay energies. If the N_c>1000 and SNR>0.3, which is defined as the minimum identifiable condition, the averaged identification accuracy is 99.87%. Even when the source and the detector are covered with lead bricks and the response function of the detector thus varies, the ANN which was trained using non-shielding spectra still shows high accuracy as long as the minimum identifiable condition is satisfied. Among all the considered nuclides, only the identification accuracy of ²³⁵U is seriously affected by the shielding. Identification of other nuclides shows high accuracy even the shielding condition is changed, which indicates that the ANN has good generalization performance.

• Journal of the Korean Society for information Management
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• v.26 no.2
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• pp.195-210
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• 2009

This study explores knowledge structures of science and technology disciplines using a cocitation analysis of journal subject categories with the publication data of a science & technology oriented university in Korea. References cited in the articles published by the faculty of the university were analyzed to produce MDS maps and network centralities. For the whole university research domain, six clusters were created including clusters of Biology related subjects, Medicine related subjects, Chemistry plus Engineering subjects, and multidisciplinary sciences plus other subjects of multidisciplinary nature. It was found that subjects of multidisciplinary nature and Biology related subjects function as central nodes in knowledge communication network in science and technology. Same analysis procedure was applied to two natural science disciplines and another two engineering disciplines to present knowledge structures of the departmental research domains.

• Journal of Information Management
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• v.21 no.2
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• pp.9-23
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• 1990

CD-ROM technique, not more than 10 years after development, goes through rapid growth, has been taken advantage of several practical application parts. Needless to say about bibliographic data, numeric value, the phonetics, an image and a picture data that are recorded as abstract or full text, and offered and applied to industry, information service including library, it can be used for library staffs, information retrieval. Escape from the need of one disc drive and one computer to access one disc, now we organize an ideal system that can be retrieved several CD-ROM used only one drive, several users can access several information, so networking is possible through LAN. In this article, we studied the function and type, characteristics, system, structure, data block, production procedure, standardization of CD-ROM LAN.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.60.2-61
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• 2017

A Convolutional Neural Network(CNN) is one of the well-known deep-learning methods in image processing and computer vision area. In this study, we apply CNN to two kinds of flare forecasting models: flare classification and occurrence. For this, we consider several pre-trained models (e.g., AlexNet, GoogLeNet, and ResNet) and customize them by changing several options such as the number of layers, activation function, and optimizer. Our inputs are the same number of SOHO)/MDI images for each flare class (None, C, M and X) at 00:00 UT from Jan 1996 to Dec 2010 (total 1600 images). Outputs are the results of daily flare forecasting for flare class and occurrence. We build, train, and test the models on TensorFlow, which is well-known machine learning software library developed by Google. Our major results from this study are as follows. First, most of the models have accuracies more than 0.7. Second, ResNet developed by Microsoft has the best accuracies : 0.77 for flare classification and 0.83 for flare occurrence. Third, the accuracies of these models vary greatly with changing parameters. We discuss several possibilities to improve the models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12C
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• pp.181-189
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• 2001

In this thesis, a policing algorithm is proposed, which is one of the traffic management function in ATM networks. The proposed algorithm minimizes CLR(Cell Loss patio) of high priority cells and solves burstiness problem of the traffic caused by multiplexing and demultiplexing process. The proposed algorithm has been implemented with VHDL and is divided into three parts, which are an input module, an UPC module, and an output module. In implementation of the UPC module\`s memory access, memory address is assigned according to VCI\`s LSB(Lowest Significant Byte) of ATM header for convenience. And the error of VSA operation from counter\`s wrap-around can be recovered by the proposed method. ANAM library 0.25 $\mu\textrm{m}$ and design compiler of Synopsys are used for synthesis of the algorithm and Synopsys VSS tool is used for VHDL simulation of it

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.30-37
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• 2021

Various devices are connected to the Internet, and attacks using the Internet are occurring. Among such attacks, there are attacks that use malicious URLs to make users access to wrong phishing sites or distribute malicious viruses. Therefore, how to detect such malicious URL attacks is one of the important security issues. Among recent deep learning technologies, neural networks are showing good performance in image recognition, speech recognition, and pattern recognition. This neural network can be applied to research that analyzes and detects patterns of malicious URL characteristics. In this paper, performance analysis according to various parameters was performed on a method of detecting malicious URLs using neural networks. In this paper, malicious URL detection performance was analyzed while changing the activation function, learning rate, and neural network structure. The experimental data was crawled by Alexa top 1 million and Whois to build the data, and the machine learning library used TensorFlow. As a result of the experiment, when the number of layers is 4, the learning rate is 0.005, and the number of nodes in each layer is 100, the accuracy of 97.8% and the f1 score of 92.94% are obtained.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.296-304
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• 2016

Recently in domestic market, the telecommunication and appliance companies actively provide IoT home service through their dedicated smart device and communication network. But because their service should use only their own devices and be payed by monthly, it does not satisfy user's needs. So, users want device and service environment that can be easily configured according to user needs. Therefore, in this paper, we propose IoT home service environment architecture and ACOME(Auto-Configuration of MQTT and REST) mechanism. The proposed architecture consists of IoT platform and IoT home gateway. And the ACOME provides the automatic registration using DPWS function and interface construction using MQTT. This implements as a library for open-source hardware such as Arduino that is easy to get on the market. So the user easy to make own IoT device. Finally, we provide performance evaluation about service and device discovery between ACOME and DPWS.