• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.81-86
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• 2002

In current CBR(Case-Based Reasoning) systems, the case adaptation is usually performed by rule-based method that use rules hand-coded by the system developer. So, CBR system designer faces knowledge acquisition bottleneck similar to those found in traditional expert system design. In this thesis, 1 present a model for learning method of case adaptation knowledge using case base. The feature difference of each pair of cases are noted and become the antecedent part of an adaptation rule, the differences between the solutions in the compared cases become the consequent part of the rule. However, the number of rules that can possibly be discovered using a learning algorithm is enormous. The first method for finding cases to compare uses a syntactic measure of the distance between cases. The threshold fur identification of candidates for comparison is fixed th the maximum number of differences between the target and retrived case from all retrievals. The second method is to use similarity metric since the threshold method may not be an accurate measure. I suggest the elimination method of duplicate rules. In the elimination process, a confidence value is assigned to each rule based on its frequency. The learned adaptation rules is applied in riven target Problem. The basic. process involves search for all rules that handle at least one difference followed by a combination process in which complete solutions are built.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3378-3393
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• 2014

We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today's data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1431-1437
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• 2011

This paper proposes a novel watermarking algorithm that protects digital copyrights and is robust to attacks. Watermarks are embedded in the subband including the significant part of the signal such as a pitch. Generally, the subband containing the pitch has the biggest energy. In order to find this subband, wavelet packet transform is used to decompose the subbands and their energy are calculated. The signal of the selected subbands is transformed in frequency domain using FFT. The watermarks are embedded using QIM for samples higher than a certain threshold. The blind detection uses the Euclidean distance. The proposed method shows less than 5% BER in the audio watermark benchmarking.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.585-592
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• 2017

Recently, as the air traffic volume has been explosively increased, various studies are being conducted to increase the air passenger capacity. In order to compensate the limitation of the separation distance on the aerodrome established on the basis of existing VOR / DME equipment, GNSS utilizing satellite is considered. In addition, we are trying to obtain more precise location information by using SBAS, a system that can correct GNSS error. ICAO recommends introducing SBAS until 2025, and Korea has also started to develop KASS, a Korean SBAS since 2014. Therefore, in this paper, we analyze the interference threshold for the measurement items and the receiving antenna gain according to the elevation angle of the satellite receiving antenna.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.139-147
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• 2009

In this paper, optimum design of energy-aware distributed detection is considered for a parallel sensor network system consisting of a fusion center and two passive sonar nodes. AND rule and OR rule are employed as the fusion rules of the sensor network. For the fusion rules, it is shown that a threshold rule of each sensor node has uniformly most powerful properties. Optimum threshold for each sensor is investigated that maximizes the probability of detection under a constraint on energy consumption due to false alarms. It is also investigated through numerical experiments how signal strength, an energy constraint, and the distance between two sensor nodes affect the system detection performances.

• Imaging Science in Dentistry
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• v.48 no.2
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• pp.111-119
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• 2018

Purpose: This study was conducted to evaluate the accuracy of linear measurements of 3-dimensional (3D) images generated by cone-beam computed tomography (CBCT) and facial scanning systems, and to assess the effect of scanning parameters, such as CBCT exposure settings, on image quality. Materials and Methods: CBCT and facial scanning images of an anthropomorphic phantom showing 13 soft-tissue anatomical landmarks were used in the study. The distances between the anatomical landmarks on the phantom were measured to obtain a reference for evaluating the accuracy of the 3D facial soft-tissue images. The distances between the 3D image landmarks were measured using a 3D distance measurement tool. The effect of scanning parameters on CBCT image quality was evaluated by visually comparing images acquired under different exposure conditions, but at a constant threshold. Results: Comparison of the repeated direct phantom and image-based measurements revealed good reproducibility. There were no significant differences between the direct phantom and image-based measurements of the CBCT surface volume-rendered images. Five of the 15 measurements of the 3D facial scans were found to be significantly different from their corresponding direct phantom measurements(P<.05). The quality of the CBCT surface volume-rendered images acquired at a constant threshold varied across different exposure conditions. Conclusion: These results proved that existing 3D imaging techniques were satisfactorily accurate for clinical applications, and that optimizing the variables that affected image quality, such as the exposure parameters, was critical for image acquisition.

• Journal of Satellite, Information and Communications
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• v.9 no.2
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• pp.23-27
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• 2014

In this paper, we propose a soft demapping method for 64-ary APSK in the DVB-S3 system. The proposed method in this paper uses the hard decision threshold (HDT) line for each constituent bit in a symbol, and calculates the soft bit information with the distance between the HDT line and the detected symbol. If the HDT lines are defined in a simple manner, the complexity to estimate soft information can be largely reduced compared with the maximum likelihood detection (MLD) which has an exponential complexity. By considering this, we first derive HDT lines for each constituent bit for a 64-APSK symbol, and propose a method to calculate soft bit information. We simulate the BER performance of the proposed scheme by using a turbo codes which requires soft-input-soft-output information, and compare it that of the MLD. The result show that the proposed scheme produces approximating performance to MLD with largely reduced complexity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.142-148
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• 2015

Due to advancements in technology and manufacturing capability, it is not uncommon that life tests yield no or few failures at low stress levels. In these situations it is difficult to analyse lifetime data and make meaningful inferences about product or system reliability. For some products or systems whose performance characteristics degrade over time, a failure is said to have occurred when a performance characteristic crosses a critical threshold. The measurements of the degradation characteristic contain much useful and credible information about product or system reliability. Degradation measurements of the performance characteristics of an unfailed unit at different times can directly relate reliability measures to physical characteristics. Reliability prediction based on physical performance measures can be an efficient and alternative method to estimate for some highly reliable parts or systems. If the degradation process and the distance between the last measurement and a specified threshold can be established, the remaining useful life is predicted in advance. In turn, this prediction leads to just in time maintenance decision to protect systems. In this paper, we describe techniques for mapping product or system which has degrading performance parameter to the associated classical reliability measures in the performance domain. This paper described a general modeling and analysis procedure for reliability prediction based on one dominant degradation performance characteristic considering pseudo degradation performance life trend model. This pseudo degradation trend model is based on probability modeling of a failure mechanism degradation trend and comparison of a projected distribution to pre-defined critical soft failure point in time or cycle.

• Journal of Wetlands Research
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• v.21 no.1
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• pp.16-26
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• 2019

Wetlands that provide diverse ecosystem services, such as habitat provision and hydrological control of flora and fauna, constitute ecosystems through interaction between wetlands existing in a wetlandscape. Therefore, to evaluate the wetland functions such as resilience, it is necessary to analyze the ecological connectivity that is formed between wetlands which also show hydrologically dynamic behaviors. In this study, by defining wetlands as ecological nodes, we generated ecological networks through the connection of wetlands according to the dispersal model of wetland species. The characteristics of these networks were then analyzed using various network metrics. In the case of the dispersal based on a threshold distance, while a high local clustering is observed compared to the exponential dispersal kernel and heavy-tailed dispersal model, it showed a low efficiency in the movement between wetlands. On the other hand, in the case of the stochastic dispersion model, a low local clustering with high efficiency in the movement was observed. Our results confirmed that the ecological network characteristics are completely different depending on which dispersal model is chosen, and one should be careful on selecting the appropriate model for identifying network properties which highly affect the interpretation of network structure and function.

• Journal of IKEEE
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• v.22 no.4
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• pp.1140-1146
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• 2018

Recently, a methodology for analyzing complex bio-signals using a deep learning model has emerged among studies that recognize human emotions. At this time, the accuracy of emotion classification may be changed depending on the evaluation method and reliability depending on the kind of data to be learned. In the case of biological signals, the reliability of data is determined according to the noise ratio, so that the noise detection method is as important as that. Also, according to the methodology for defining emotions, appropriate emotional evaluation methods will be needed. In this paper, we propose a wavelet -based noise threshold setting algorithm for verifying the reliability of data for multimodal bio-signal data labeled Valence and Arousal and a method for improving the emotion recognition rate by weighting the evaluation data. After extracting the wavelet component of the signal using the wavelet transform, the distortion and kurtosis of the component are obtained, the noise is detected at the threshold calculated by the hampel identifier, and the training data is selected considering the noise ratio of the original signal. In addition, weighting is applied to the overall evaluation of the emotion recognition rate using the euclidean distance from the median value of the Valence-Arousal plane when classifying emotional data. To verify the proposed algorithm, we use ASCERTAIN data set to observe the degree of emotion recognition rate improvement.