• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.232-235
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• 1998

This paper evaluates the performance of a rough set based pattern classifier using the benchmarks in artificial neural nets depository found in internet. The definition of rough set in soft computing paradigm is briefly introduced. next the design of rough set classifier is suggested. Finally benchmark test results are shown the performance of rough set compare to that of ANNs and decision tree.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.24-30
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• 2021

The accelerated growth of the internet and the enormous amount of data availability has become the primary reason for machine learning applications for data analysis and, more specifically, pattern recognition and decision making. In this paper, we focused on the crowdfunding site Kickstarter and collected the comments in order to apply neural networks to classify the projects based on the sentiments of backers. The power of customer reviews and sentiment analysis has motivated us to apply this technique in crowdfunding to find timely indications and identify suspicious activities and mitigate the risk of money loss.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.2
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• pp.268-277
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• 2001

In this paper, the system performance with the convolution code using a Viterbi decoding and the one tap LMS equalizer applied to the OFDM system, which is suitable for IEEE 802.1la wireless LAN in indoor wireless channel, is analyzed through computer simulation. Indoor wireless channel is modeled as Rician fading channel, and QPSK and 16QAM scheme are used for subchannel modulation. In Rician fading channel with the power ratio of the direct path signal to the scattered signals, K=5 dB, BER of $10^{-4}$ is satisfied if the SNRs of the QPSK/OFDM and the 16QAM/OFDM are 8.6 dB and 19.2 dB in hard decision and 5.3 dB and 9.8 dB in soft decision, respectively. Compared with convolution code scheme, it is observed that 16QAM/OFDM system with the one tap LMS equalizer has the performance improvement of 8.6 dB and 2 dB in hard decision and soft decision, respectively.

• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.82-92
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• 2012

This study is on the color expression of 100 male bike helmet products examining color image scale with high preference, PCCS color system applied color analysis and influence of color in decision making. The targets are all domestically distributed male bike helmets. The image scale was divided into 4 sections (Soft, Hard, Dynamic and Static) by color, and color image scale was analyzed to top 10 priority products. And analysis according to PCCS color system was made. Finally, questionnaire survey was carried out to analyze the influence of color on purchase decision making. The questionnaire survey was carried out to male in 20s~50s who were the member of 18 bike clubs in S agent in Seoul. 414 out of 422 sheets except for 8 insufficient ones were used. The results can be divided into 3.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.3
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• pp.127-139
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• 2014

In this paper, we present two signal processing techniques for designing binary error correction codes for Multi-Level Cell(MLC) NAND flash memory. MLC NAND flash memory saves the non-binary symbol at each cell and shows asymmetric channel LLR l-density which makes it difficult to design soft-decision binary error correction codes such as LDPC codes and Polar codes. Therefore, we apply density mirroring and EM algorithm for approximating the MLC NAND flash memory channel to the binary-input memoryless channel. The density mirroring processes channel LLRs to satisfy roughly all-zero codeword assumption, and then EM algorithm is applied to l-density after density mirroring for approximating it to mixture of symmetric Gaussian densities. These two signal processing techniques make it possible to use conventional code design algorithms, such as density evolution and EXIT chart, for MLC NAND flash memory channel.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.5
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• pp.451-458
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• 2014

This study is a practical research for setting a process of making references of design decision and guidlines of limitation in the movement from the design development to the construction design by StrAuto. StrAuto, as a parametric modeling and optimization tool for building structure, enables a quantity of design cases to be analyzed automatically by changing parameters of sturctural properties. So the designer using StrAuto can check a lot of analysis data crossing thousands of cases, see which case is out of acceptable range, and make a decision for design and optimization. In this thesis, the application of StrAuto optimization process to the residence tower UIC project shows the practical applicability in the construction design and value engineering. StrAuto optimized ideally volume of core walls by 31.3% and lead the final revised model applied to the construction design to reduce volume by 18.1%. The significance of this research is the implementation of process that the designer can quickly review a number of cases and get a direction for construction design and optimization after design development.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.90-97
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• 2006

In this paper, we present a cost-effective architecture of high-speed soft-decision Viterbi decoder for Multi-band OFDM(MB-OFDM) systems. In the design of modem for MB-OFDM systems, a parallel processing architecture is general]y used for the reliable hardware implementation, because the systems should support a very high-speed data rate of at most 480Mbps. A Viterbi decoder also should be designed by using a parallel processing structure and support a very high-speed data rate. Therefore, we present a optimized hardware architecture for 4-way parallel processing Viterbi decoder in this paper. In order to optimize the hardware of Viterbi decoder, we compare and analyze various ACS architectures and find the optimal one among them with respect to hardware complexity and operating frequency The Viterbi decoder with a optimal hardware architecture is designed and verified by using Verilog HDL, and synthesized into gate-level circuits with TSMC 0.13um library. In the synthesis results, we find that the Viterbi decoder contains about 280K gates and works properly at the speed required in MB-OFDM systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.951-958
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• 2017

In an attack context, the adversary wants to retrieve the message from the intercepted noisy bit stream without any prior knowledge of the channel codes used. The process of finding out the code parameters such as code length, dimension, and generator, for this purpose, is called the blind recognition of channel codes or the reconstruction of channel codes. In this paper, we suggest an improved algorithm of the blind recovery of rate k/n convolutional encoders in a noisy environment. The suggested algorithm improves the existing algorithm by Marazin, et. al. by evaluating the threshold value through the estimation of the channel error probability of the BSC. By applying the soft decision method by Shaojing, et. al., we considerably enhance the success rate of the channel reconstruction.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.53-59
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• 2008

Recently, vision-based hand gesture recognition techniques have been developed for assisting elderly and disabled people to control home appliances. Frequently occurred problems which lower the hand gesture recognition rate are due to the inter-person variation and intra-person variation. The recognition difficulty caused by inter-person variation can be handled by using user dependent model and model selection technique. And the recognition difficulty caused by intra-person variation can be handled by using fuzzy logic. In this paper, we propose multivariate fuzzy decision tree learning and classification method for a hand motion recognition system for multiple users. When a user starts to use the system, the most appropriate recognition model is selected and used for the user.

• The Journal of Korea Robotics Society
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• v.3 no.2
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• pp.81-90
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• 2008

While increasing demand of the service for the disabled and the elderly people, assistive technologies have been developed rapidly. The natural signal of human such as voice or gesture has been applied to the system for assisting the disabled and the elderly people. As an example of such kind of human robot interface, the Soft Remote Control System has been developed by HWRS-ERC in $KAIST^[1]$. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and intra-person variation. Intra-person variation can be handled by inducing fuzzy concept. In this paper, we propose multivariate fuzzy decision tree(MFDT) learning and classification algorithm for hand motion recognition. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user's hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. For the performance of hand gesture recognition, we tested using hand gesture data which is collected from 10 people for 15 days. The experimental results show that the classification and user adaptation performance of proposed algorithm is better than general fuzzy decision tree.