• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.704-712
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• 2010

Recently, the lack of the frequency have been emerged due to the use of various wireless terminals. To find solutions for obtaining new frequency resources and for avoiding an interference, a cognitive radio technology have focused on as the new technology that can use the whitespace in TV broadcasting band. In this paper, the Cognitive Radio terminal system for whitespace in TV band is first introduced. The purpose of the CR terminal platform is to evaluate the feasibility of the unlicensed wireless service in the TV band. Then, we design and develop the software for Sensing MAC applied the cognitive radio terminal system. The developed Sensing MAC software based on IEEE 802.22 specification can be aware of using the whitespace in TV band that operates on cognitive radio terminal system. Finally, we have tested for evaluating the sensing MAC module.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.11-17
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• 2007

One of the popular methods used for pattern classification is the MBR (Memory-Based Reasoning) algorithm. Since it simply computes distances between a test pattern and training patterns or hyperplanes stored in memory, and then assigns the class of the nearest training pattern, it cannot explain how the classification result is obtained. In order to overcome this problem, we propose an incremental teaming algorithm based on RPA (Recursive Partition Averaging) to extract IF-THEN rules that describe regularities inherent in training patterns. But rules generated by RPA eventually show an overfitting phenomenon, because they depend too strongly on the details of given training patterns. Also RPA produces more number of rules than necessary, due to over-partitioning of the pattern space. Consequently, we present the IREA (Incremental Rule Extraction Algorithm) that overcomes overfitting problem by removing useless conditions from rules and reduces the number of rules at the same time. We verify the performance of proposed algorithm using benchmark data sets from UCI Machine Learning Repository.

• Journal of KIISE
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• v.43 no.9
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• pp.998-1007
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• 2016

In recent years, the cost of automotive ECU (Electronic Control Unit) has accounted for more than 30% of total car production cost. However, the complexity of testing and debugging an automotive ECU is increasing because automobile manufacturers outsource automotive ECU production. Therefore, a large amount of cost and time are spent to localize faults during testing an automotive ECU. In order to solve these problems, we propose a fault localization method in memory for developers who run the integration testing of automotive ECU. In this method, memory is partitioned by utilizing memory map, and fault-suspiciousness for each partition is calculated by utilizing memory update information. Then, the fault-suspicious region for partitions is decided based on calculated fault-suspiciousness. The preliminary result indicated that the proposed method reduced the fault-suspicious region to 15.01(%) of memory size.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.140-152
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• 2003

An XML document has a structure which may be irregular. It is difficult for end-users to comprehend the irregular document structure exactly. For these XML documents, an end-user has a difficulty in using structured query. Therefore, an end-user formulates no structured query or a query which has a little structure information. In this context, we propose new retrieval models which use the structured information for ranking and compensate the difference between user query structure and document structure. To ease with querying, we assume the independence among querying paths which represent structural constraints. Since this assumption makes degradation of the expression power of a query language, we also propose a model which overcome this problem. As there had been no test collections for XML documents, we made a small test collection from TIPSTER of the RTEC and experimented on this collection without a structured query, From this experiment, we showed that our models improve average precision about 67% over conventional Vector-Space model.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.153-160
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• 2020

Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.1-10
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• 2008

In this paper, we designed a facial feature(eyes, a moult and a nose) detection hardware based on the ICT transform which was developed for face detection earlier. Our design used a pipeline architecture for high throughput and it also tried to reduce memory size and memory access rate. The algerian and its hardware implementation were tested on the BioID database, which is a worldwide face detection test bed, and its facial feature detection rate was 100% both in software and hardware, assuming the face boundary was correctly detected. After synthesizing the hardware on Dongbu $0.18{\mu}m$ CMOS library, its die size was $376,821{\mu}m^2$ with the maximum operating clock 78MHz.

• Journal of the Korean Society of Safety
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• v.25 no.6
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• pp.203-209
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• 2010

A compact ANSI/EIA 709.1 protocol analyzer system suited for maintenance of LonWorks network for safety management of underground facilities was developed and tested. The hardware is based on the TMS320LF2406A embedded system, and the software was designed using Visual C++6.0 under Windows XP environment. Connected to the LonWorks network the developed protocol analyzer decodes the raw packets and pass them to the master PC through USB port. Then on the PC the packets are processed and analyzed in various aspects and the key features that are essential to the maintenance of LonWorks network installed at underground facilities are displayed in a user-friendly format. Performance of the developed protocol analyzer was evaluated through a series of experiments, by measuring the speed of packet analysis and the error rate. The protocol analyzer proved to work reliably even under the increased bandwidth. However, more comprehensive tests under various underground environmental conditions are desired.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.5
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• pp.57-68
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• 2014

MSRDS(Microsoft Robotics Developer Studio), the robot simulation platform provides the simulation robots and environments enabling to the basic robot programming without hardware robots. In this paper, we carry the maze escaping problems to compare and analyze the performance of LRF, bumper, IR, and sonar sensor with the same condition on MSRDS(Microsoft Robotics Developer Studio) environment. To evaluate the performance of sensors, we program the simulation environments with same conditions for all sensors. We could find that the LRF sensor had the highest performance and the bumper sensor has the lowest performance on the travel time, the number of turning, and the number of collisions. It was also confirmed that IR sensor and sonar sensor had lower performance than LRF sensor on the number of turning.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1225-1234
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• 2019

The security of the firmware is more important because embedded devices have become popular. Network devices such as routers can be attacked by attackers through web application vulnerabilities in embedded firmware. Therefore, they must be found and removed quickly. The Firmadyne framework proposes a dynamic analysis method to find vulnerabilities after emulating firmware. However, it only performs vulnerability checks according to the analysis methods defined in the tool, thus limiting the scope of vulnerabilities that can be found. In this paper, fuzzing is performed in emulation-based environment through fuzzing, one of the software security test techniques. We also propose a Fabfuzz tool for efficient emulation based fuzzing. Experiments have shown that in addition to the vulnerabilities identified in existing tools, other types of vulnerabilities have been found.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.681-687
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• 2007

Face recognition technique is very popular for a personal information security and user identification in recent years. However, the face recognition system is very hard to be implemented due to the difficulty where change in illumination, pose and facial expression. In this paper, we consider that an illumination change causing the variety of face appearance, virtual image data is generated and added to the D-LDA which was selected as the most suitable feature extractor. A less sensitive recognition system in illumination is represented in this paper. This way that consider nature of several illumination directions generate the virtual training image data that considered an illumination effect of the directions and the change of illumination density. As result of experiences, D-LDA has a less sensitive property in an illumination through ORL, Yale University and Pohang University face database.