• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.326-330
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• 2008

The study on combining multiple classifiers in the field of pattern recognition has mainly focused on how to combine multiple classifiers, but it has gradually turned to the study on how to select multiple classifiers from a classifier pool recently. Actually, the performance of multiple classifier system depends on the selected classifiers as well as the combination method of classifiers. Therefore, it is necessary to select a classifier set showing good performance, and an approach based on information theory has been tried to select the classifier set. In this paper, a classifier set candidate is made by the selection of classifiers, on the basis of mutual information between classifiers, and the classifier set candidate is compared with the other classifier sets chosen by the different selection methods in experiments.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1229-1233
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• 2010

In event-driven wireless sensor networks, network congestion occurs when event data, which have higher transmission rates than periodic sensing data, arc forwarded to bottleneck links. As the congestion continues, congestion collapse is triggered, so most of packets from source nodes are failed to transmit to a sink node. Rate control schemes can be a solution for preventing the congestion collapse problem. In this paper, a rate control scheme that each node controls child node's data rate based on congestion patterns is proposed. Experiments show that the proposed scheme effectively controls network congestion and successfully transmits more event data packets to a sink node than existing rate control schemes.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.8
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• pp.837-841
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• 2010

This paper proposes an effective similarity measure for content-based image retrieval using MPEG-7 DCD. The proposed method can measure the similarity of images with the percentage of dominant colors extracted from images. As the result of experiments, we achieved a significant improvement of 18.92% with global DCD and 47.22% with local DCD in ANMRR than the result by QHDM. This result shows that the proposed method is an effective similarity measure for content-based image retrieval. Especially, our method is useful for region-based image retrieval.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.497-501
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• 2008

In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classily multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.654-662
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• 2016

With the advent of autonomous cars, we explored the problems which will soon arise while parking in car parks. These include structure of parking lot suitable for autonomous cars, finding the closest parking slot available, and navigation to the location. We provide an initial solution, wherein we use a central server and the graph of the parking lot to guide cars to the closest parking slots available. Our experiments have shown that the proposed method is effective for the controlled parking for self-driving cars.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.742-746
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• 2010

In this paper, we propose new randomness testing methods based on approximate periods in order to improve the previous randomness testing method using exact pattern matching. Finding approximate periods of random sequences enables us to search similarly repeated parts, but it has disadvantages since it takes long time. In this paper we propose randomness testing methods whose time complexity is O($n^2$) by reducing the time complexity of computing approximate periods from O($n^3$) to O($n^2$). Moreover, we perform some experiments to compare pseudo random number generated by AES cryptographic algorithms and true random number.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.3
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• pp.24-34
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• 2005

The automobile seat must satisfy various safety regulations for the passenger's safety. In many design practices, each component is independently designed by concentrating on a single related regulation. However, since multiple regulations can be involved in a seat component, there may be design confliction among the various safety regulations. Therefore, a new design methodology is required to effectively design an automobile seat. The axiomatic approach is employed for considering multiple regulations. The Independence Axiom is used to define the overall flow of the seat design. Functional requirements (FRs) are defined by safety regulations and components of the seat are classified into groups which yield design Parameters (DPs). The classification is carried out to have independence in the FR-DP relationship. Components in a DP group are determined by using orthogonal away of the design of experiments (DOE). Numerical analyses are utilized to evaluate the safety levels by using a commercial software system for nonlinear transient finite element analysis.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.101-105
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• 2008

This paper proposes an analytical model to calculate VoIP (Voice of IP) capacity over wireless LANs with frequent bit errors. Since the traditional analytical models for VoIP capacity have not included the effect of bit errors, simulations ould only evaluate VoIP capacity over erroneous channels. For analytically accurate estimation of VoIP capacity over noisy channels, we extend the conventional model to include the effect of propagation errors, end-to-end delay, voice quality, the waiting time in AP(Access Point). The experiments show that our model predicts the VoIP capacity of a given network within the range from 3% to 9% difference comparing with the simulation results.

• KIISE Transactions on Computing Practices
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• v.22 no.10
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• pp.513-520
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• 2016

As IoT devices become more common in the public sphere, the energy efficiency of embedded systems becomes a problem of major interest in addition to the system performance. Energy efficiency is important for portable embedded systems because they obtain power from their battery, and a low energy efficiency will result in a low usage time while a high energy efficiency will allow for longer usage time. In this paper, we propose a memory usage based frequency selection method to improve the energy efficiency of embedded Linux systems by using devfreq to select the device's system frequency. In our experiments, we found that the proposed method reduces energy consumption in an embedded device by up to 18%.

• KIISE Transactions on Computing Practices
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• v.20 no.9
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• pp.483-491
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• 2014

Contemporary computer systems exploits DVFS (Dynamic Voltage/Frequency Scaling) technology for balancing performance and power consumption. The efficiency of DVFS depends on how much performance we get for larger power consumption due to elevated CPU frequency. Especially for memory-bounded applications, higher CPU frequency often does not result in higher performance. In this paper, we present an upper bound of CPU frequency scaling based on memory accesses. It is observed that the performance gain due to higher CPU frequency is limited by memory accesses (last level cache misses) per instructions by experiments. Using the results, we present the CPU frequency upper bound with little performance gain. Experimental results show that for a memory-bounded application, applying the frequency upper bound enhances the energy efficiency of the application by above 30%.