• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9B
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• pp.939-946
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• 2009

In this paper, we propose a new network ASIP(Application Specific Instruction-set Processor) which was designed for simulation models by a machine descriptions language LISA(Language for Instruction Set Architecture). This network ASIP is aimed for an exclusive engine undertaking packet processing in a router. To achieve the purpose, we added a new necessary instruction set for processing a general ASIP based on MIPS(Microprocessor without Interlock Pipeline Stages) architecture in high speed. The new instructions can be divided into two groups: a classification instruction group and a modification instruction group, and each group is to be processed by its own functional unit in an execution stage. The functional unit was optimized for area and speed through Verilog HDL, and the result after synthesis was compared with the area and operation delay time. Moreownr, it was allocated to the Macro function ana low-level standardized programming language C using CKF(Compiler Known Function). Consequently, we verified performance improvement achieved by analysis and comparison of execution cycles of application programs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.523-531
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• 2003

Java applets are downloaded from web server through internet and executed in Java Virtual Machine of clients'browser. Before execution of java applets, JVM checks bytecode program with bytecode verifier and performs runtime tests with interpreter. However, these tests will not protect against undesirable runtime behavior of java applets, such as denial of service attack, email forging attack, URL spoofing attack, or annoying sound attack. In order to protect malicious applets, a technique used in this paper is java bytecode modification. This technique is used to restrict applet behavior or insert code appropriate to profiling or other monitoring efforts. Java byte modification is divided into two general forms, class-level modification involving subclassing non-final classes and method-level modification used when control over objects from final classes or interface. This paper showed that malicious applets are controlled by java bytecode modification using proxy server. This implementation does not require any changes in the web sever, JVM or web browser.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.61-69
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• 2015

Through Virtual Machine technology(VM), VMs can be packed into much fewer number of physical servers than that of VMs. Since even an idle physical server wastes more than 60% of max power consumption, it has been considered as one of energy saving technologies to minimize the number of physical servers by using the knapsack problem solution based on the computing resources. However, this paper shows that this tightly packed consolidation may not achieve the efficient energy saving. Instead, a service pattern-based VM consolidation algorithm is proposed. The proposed algorithm takes the service time of each VM into account, and consolidates VMs to physical servers in the way to minimize energy consumption. The comprehensive simulation results show that the proposed algorithm gains more than 30% power saving.

• ETRI Journal
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• v.22 no.1
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• pp.1-11
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• 2000

The Satellite Operation System (SOS) has been developed for a low earth orbiting remote sensing satellite, Korea Multipurpose Satellite-I, to monitor and control the spacecraft as well as to perform the mission operation. SOS was designed to operate on UNIX in the HP workstations. In the design of SOS, flexibility, reliability, expandability and interoperability were the main objectives. In order to achieve these objectives, a CASE tool, a database management system, consultative committee for space data systems recommendation, and a real-time distributed processing middle-ware have been integrated into the system. A database driven structure was adopted as the baseline architecture for a generic machine-independent, mission specific database. Also a logical address based inter-process communication scheme was introduced for a distributed allocation of the network resources. Specifically, a hotstandby redundancy scheme was highlighted in the design seeking for higher system reliability and uninterrupted service required in a real-time fashion during the satellite passes. Through various tests, SOS had been verified its functional, performance, and inter-face requirements. Design, implementation, and testing of the SOS for KOMPSAT-I is presented in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.285-307
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• 2009

This paper introduces DICE, a Domain-Independent text Classification Engine. DICE is robust, efficient, and domain-independent in terms of software and architecture. Each module of the system is clearly modularized and encapsulated for extensibility. The clear modular architecture allows for simple and continuous verification and facilitates changes in multiple cycles, even after its major development period is complete. Those who want to make use of DICE can easily implement their ideas on this test bed and optimize it for a particular domain by simply adjusting the configuration file. Unlike other publically available tool kits or development environments targeted at general purpose classification models, DICE specializes in text classification with a number of useful functions specific to it. This paper focuses on the ways to locate the optimal states of a practical text classification framework by using various adaptation methods provided by the system such as feature selection, lemmatization, and classification models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4090-4102
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• 2018

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

• Journal of the Korea Institute of Military Science and Technology
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• v.5 no.2
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• pp.123-131
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• 2002

ASC(Avionics System Computer) was developed to control weapon delivery and navigation sensors, and to perform man-machine interface with pilots for XKO-1 aircraft. The data communications between ASC and UFC(Up Front Controller), DC(Data Concentrator) were implemented by RS422 serial data bus. Also, SCIL(Standard Computer Interface Library) was designed to facilitate control and management of the computer hardware resources and is embedded in the ASC. These structures have a merit of noise immunity and a reduction of wire harness for signal lines, and enable OFP(Operational Flight Program) programmers to use the SCIL easily without knowing hardware details. Manufactured system was on installed on XKO-1, and peformed for BIT(Built In Test) and interface test with UFC and DC. The test results show that it meets the system requirements.

• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.79-85
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• 2017

Understanding of human emotion has a high importance in interaction between human and machine communications systems. The most expressive and valuable way to extract and recognize the human's emotion is by facial expression analysis. This paper presents and implements an automatic extraction and recognition scheme of facial expression and emotion through still image. This method has three main steps to recognize the facial emotion: (1) Detection of facial areas with skin-color method and feature maps, (2) Creation of the Bezier curve on eyemap and mouthmap, and (3) Classification and distinguish the emotion of characteristic with Hausdorff distance. To estimate the performance of the implemented system, we evaluate a success-ratio with emotional face image database, which is commonly used in the field of facial analysis. The experimental result shows average 76.1% of success to classify and distinguish the facial expression and emotion.

• Communications for Statistical Applications and Methods
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• v.30 no.5
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• pp.517-530
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• 2023

Recent studies revealed that fine dust in ambient air may cause various health problems such as respiratory diseases and cancer. To prevent the toxic effects of fine dust, it is important to predict the concentration of fine dust in advance and to identify factors that are closely related to fine dust. In this study, we developed a Bayesian network model for predicting PM10 concentration in Seoul, Korea, and visualized the relationship between important factors. The network was trained by using air quality and meteorological data collected in Seoul between 2018 and 2021. The study results showed that current PM10 concentration, season, carbon monoxide (CO) were the top 3 effective factors in 24 hours ahead prediction of PM10 concentration in Seoul, and that there were interactive effects.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.105-127
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• 2024

We propose recurrent neural network (RNN) methods for forecasting realized volatility (RV). The data are RVs of ten major stock price indices, four from the US, and six from the EU. Forecasts are made for relative ratio of adjacent RVs instead of the RV itself in order to avoid the out-of-scale issue. Forecasts of RV ratios distribution are first constructed from which those of RVs are computed which are shown to be better than forecasts constructed directly from RV. The apparent asymmetry of RV ratio is addressed by the Piecewise Min-max (PM) normalization. The serial dependence of the ratio data renders us to consider two architectures, long short-term memory (LSTM) and gated recurrent unit (GRU). The hyperparameters of LSTM and GRU are tuned by the nested cross validation. The RNN forecast with the PM normalization and ratio transformation is shown to outperform other forecasts by other RNN models and by benchmarking models of the AR model, the support vector machine (SVM), the deep neural network (DNN), and the convolutional neural network (CNN).