• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.427-430
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• 2002

Recently the subject of "wavelet analysis" has be drawn by both mathematical and engineering application fields such as Signal Processing, Compression/Decomposition, Wavelet-Neural Network, Statistics and etc. Even though its similar to Fourier analysis, wavelet is a versatile tool with much mathematical content and great potential for applications. Especially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. Therefore, wavelet transform has good time-analysis ability for high frequency component, and has good frequency-analysis ability for low frequency component. Using the discriminative ability is more easy method than other conventional techniques. In this paper, Morlet wavelet transform was applied to discriminate the kind of line fault by acquired data from real power transformation network. The experimental result presented that Morlet wavelet transform is easier,and more useful method than the FFT (Fast Fourier Transform).

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.317-322
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• 2011

Korean adults' internet addiction diagnosis measure, K-scale developed by Korea National Information Society Agency (NIA), has composed of 4 categories including 20 items. This scale can diagnose user's internet addiction with individual's questionnaire items. Most of previous research works were tried to know reasons of internet addiction and to judge whether adolescents are addicted or not with their samples. In this research, it is the goal to find the key component to judge individual's internet addiction by using a decision tree in the data mining field and a principal component analysis in statistics. From the experimental results, we would discover that tolerance and preoccupation factor is the most important one to affect adult's internet addiction.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.123-129
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• 2013

The aim of this study is to design an intelligent pattern-based real-time trading system (PRTS) using rough set analysis of technical indicators, dynamic time warping (DTW), and genetic algorithm in stock futures market. Rough set is well known as a data-mining tool for extracting trading rules from huge data sets such as real-time data sets, and a technical indicator is used for the construction of the data sets. To measure similarity of patterns, DTW is used over a given period. Through an empirical study, we identify the ideal performances that were profitable in various market conditions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.3
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• pp.1-11
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• 2016

The present study aims to evaluate transfer resistance factors in railway stations and draw new implications. The data used for the model estimation are RP (revealed preference) data and SP (stated preference) data. Two types of models were used for the analysis: integration model which combined line-haul stage, transfer stage and shuttle stage and separation model which assessed the three stages separately. The results revealed that while bulk freight shippers mainly focused on line-haul stage, they put emphasis on transfer stage as well. It's especially notable that transfer stage was considered more important than shuttle stage. Therefore, in future transportation policies concerning rail freight, it would be crucial not only to enhance the competitiveness of line-haul stage but also make improvements in transfer stage regarding railway stations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.417-420
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• 2007

마이크로어레이는 생명과학 분야에서 사용되는 대규모의 유전자 발현정도를 동시에 측정할 수 있는 도구이다. 마이크로어레이 실험은 많은 양의 데이터를 생성하기 때문에, 자동화된 효과적인 분석기법이 필요하다. 이 논문에서는 약물의 영향 분석을 위해 약물의 투여량 및 투여후의 시간대별로 샘플을 추출하여, 마이크로어레이를 이용하여 유전자의 발현량을 분석하는 경우에, 약물에 대해서 반응하는 유전자를 추출하는 데이터 마이닝 기법을 제안한다. 제안한 방법에서는 유전자의 발현정도값을 이전 시간의 값을 기준값으로 하여 증가, 감소, 답보에 해당하는 기호로 매핑하여, 분석자가 원하는 패턴을 보이는 유전자를 추천한다. 한편, 유전자의 상호간에 많은 영향을 주고 받기 때문에 특정 약물을 투여할 때, 이에 직접적인 영향을 받는 것도 있지만, 이와는 전혀 상관없이 동작하는 것도 있기 때문에, 제안한 방법에서는 이러한 약물 투여와 유의성이 있을 가능성이 있는 유전자만을 전처리과정을 통해서 필터링하는 기법을 활용한다. 제안한 방법은 실제 약물 투여 실험 샘플에 대한 마이크로어레이 데이터에 적용하여 활용가능성을 확인하였다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.207-216
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• 2000

Due to recent growing interest in autonomous software agents and their potential application in areas such as electronic commerce, the autonomous negotiation become more important. Evidence from both theoretical analysis and observations of human interactions suggests that if decision makers have prior information on opponents and furthermore learn the behaviors of other agents from interaction, the overall payoff would increase. We propose a new methodology for a strategy finding process using data mining in autonomous negotiation system ; ANSIA (Autonomous Negotiation System using Intelligent Agent). ANSIA is a strategy based negotiation system. The framework of ANSIA is composed of following component layers : 1) search agent layer, 2) data mining agent layer and 3) negotiation agent layer. In the data mining agent layer, that plays a key role as a system engine, extracts strategy from the historic negotiation is extracted by competitive learning in neural network. In negotiation agent layer, we propose the autonomous negotiation process model that enables to estimate the strategy of opponent and achieve interactive settlement of negotiation. ANISIA is motivated by providing a computational framework for negotiation and by defining a strategy finding model with an autonomous negotiation process.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.181-186
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• 2012

Recently there has been many active researches on searching similar sequences from data generated with the passage of time. Those data are classified as time series data or sequence data and have different semantics from scalar data of traditional databases. In this paper similar sequence search retrieves sequences that have a similar trend of value changes. At first we have transformed the original sequences by applying DFT. The converted data are more suitable for trend analysis and they require less number of attributes for sequence comparisons. In addition we have developed a region-based query and we applied bitmap indexes which could show better performance in data warehouse. We have built bitmap indexes with varying number of attributes and we have found the least cost query plans for efficient similar sequence searches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.4
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• pp.25-35
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• 2008

Variable Message Sign(VMS) is one of the subsystem of Intelligent Transportation Systems (ITS), which is useful for providing real-time information on weather, traffic and highway conditions. However, there are various situations such as incidents/accidents, constructions, special events, etc., which would be occurred on segments, it is unable to control traffic with only the VMS. Thus, it is essential to use of PVMS(Portable Variable Message Signs), which can move to the location needed traffic control and provide more active traffic information than VMS. This study developed a legibility distance model for PVMS messages using in-vehicle Differential Global Positioning Data(DGPS). Traffic conditions, drivers' characteristics, weather conditions and characteristics of PVMS message were investigated for establishing the legibility model based on multiple linear regression analysis. The factors such as height of PVMS characters, spot speed, age, gender and day and night were identified as dominants affecting the variation of legibility distances. It is expected that the proposed model would play a significant role in designing PVMS messages for providing more effective real-time traffic information.