• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.1-13
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• 2007

The necessity of future index is increasing to predict the future location of moving objects promptly for various location-based services. A representative research topic related to future index is the probability trajectory prediction technique that improves reliability using the past trajectory information of moving objects in the road network environment. However, the prediction performance of this technique is lowered by the heavy load of extensive future trajectory search in long-range future queries, and its index maintenance cost is high due to the frequent update of future trajectory. Thus, this paper proposes the Probability Cell Trajectory-Tree (PCT-Tree), a cell-based future indexing technique for efficient long-range future location prediction. The PCT-Tree reduces the size of index by rebuilding the probability of extensive past trajectories in the unit of cell, and improves the prediction performance of long-range future queries. In addition, it predicts reliable future trajectories using information on past trajectories and, by doing so, minimizes the cost of communication resulting from errors in future trajectory prediction and the cost of index rebuilding for updating future trajectories. Through experiment, we proved the superiority of the PCT-Tree over existing indexing techniques in the performance of long-range future queries.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.456-462
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• 2008

The classification is that a new data is classified into one of given classes and is one of the most generally used data mining techniques. Memory-Based Reasoning (MBR) is a reasoning method for classification problem. MBR simply keeps many patterns which are represented by original vector form of features in memory without rules for reasoning, and uses a distance function to classify a test pattern. If training patterns grows in MBR, as well as size of memory great the calculation amount for reasoning much have. NGE, FPA, and RPA methods are well-known MBR algorithms, which are proven to show satisfactory performance, but those have serious problems for memory usage and lengthy computation. In this paper, we propose DPA (Dynamic Partition Averaging) algorithm. it chooses partition points by calculating GINI-Index in the entire pattern space, and partitions the entire pattern space dynamically. If classes that are included to a partition are unique, it generates a representative pattern from partition, unless partitions relevant partitions repeatedly by same method. The proposed method has been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory and FPA, and RPA.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.623-632
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• 2018

Both investors and researchers are attentive to the prediction of stock price movement directions since the accurate prediction plays an important role in strategic decision making on stock trading. According to previous studies, taken together, one can see that different factors are considered depending on stock markets and prediction periods. This paper aims to analyze what data mining techniques show better performance with some representative index and stock price datasets in the Korea stock market. In particular, extreme gradient boosting technique, proving itself to be the fore-runner through recent open competitions, is applied to the prediction problem. Its performance has been analyzed in comparison with other data mining techniques reported good in the prediction of stock price movement directions such as random forests, support vector machines, and artificial neural networks. Through experiments with the index/price datasets of 12 years, it is identified that the gradient boosting technique is the best in predicting the movement directions after 1 to 4 days with a few partial equivalence to the other techniques.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.13-22
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• 2004

With the proliferation of multimedia data, there is an increasing need to support the indexing and retrieval of high-dimensional image data. Although there have been many efforts, the performance of existing multidimensional indexing methods is not satisfactory in high dimensions. Thus the dimensionality reduction and the approximate solution methods were tried to deal with the so-called dimensionality curse. But these methods are inevitably accompanied by the loss of precision of query results. Therefore, recently, the vector approximation-based methods such as the VA- file and the LPC-file were developed to preserve the precision of query results. However, the performance of the vector approximation-based methods depend largely on the size of the approximation file and they lose the advantages of the multidimensional indexing methods that prune much search space. In this paper, we propose a new index structure called the GC-tree for efficient similarity search in image databases. The GC-tree is based on a special subspace partitioning strategy which is optimized for clustered high-dimensional images. It adaptively partitions the data space based on a density function and dynamically constructs an index structure. The resultant index structure adapts well to the strongly clustered distribution of high-dimensional images.

• Journal of the Korean Geosynthetics Society
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• v.19 no.1
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• pp.45-53
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• 2020

There is an increasing tendency to try to make predictive analysis using measurement data based on machine learning techniques in the railway industries. In this paper, it was predicted that Track quality index (TQI) using vehicle acceleration data based on the machine learning method. The XGB (XGBoost) was the most accurate with 85% in the all data sets. Unlike the SVM model with a single algorithm, the RF and XGB model with a ensemble system were considered to be good at the prediction performance. In the case of the Surface TQI, it is shown that the acceleration of the z axis is highly related to the vertical direction and is in good agreement with the previous studies. Therefore, it is appropriate to apply the model with the ensemble algorithm to predict the track quality index using the vehicle vibration acceleration data because the accuracy may vary depending on the applied model in the machine learning methods.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.527-534
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• 2013

The software is changed according to the changing businesses and the user requirement, it involves increasing complexity and cost. Considering the repetitive changes required for the software, replacement is more efficient than maintenance at some point. In this study, the replacement time was predicted using the service dissatisfaction index and replacement point assessment index by the software group for each task. First, fuzzy inference was used to develop the method and indicator for the user's service level dissatisfaction. Second, the replacement point assessment method was established considering the quality, costs, and new technology of the software. Third, a replacement time prediction technique that used the gap between the user service measurement and replacement point assessment values was proposed. The results of the case study with the business solutions of three organizations, which was conducted to verify the validity of the proposed prediction technique in this study, showed that the service dissatisfaction index decreased by approximately 16% and the replacement point assessment index increased by approximately 9%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.267-273
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• 2021

Non-orthogonal multiple access is the promised candidates in the next generation wireless networks to improve the spectral efficiency by superposing multiple signals. In general, the superposition coding is performed using the difference in channel gain between users based on the user's power allocation. However, when user pairs have the similar channel gain problem, NOMA can not be allowed in the scenario. To overcome this problem, phase rotation based NOMA is presented to increase minimum distance between superposed signals in the constellation point. This paper proposed a novel non-orthogonal multiple access based index modulation using phase rotation. The additional bits can transfer using the index bits that is allocated according to the activated state of the phase rotation. Simulation results are shown that bit error rate and achievable sum rate are better than conventional NOMA.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.1011-1018
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• 2021

This numerical study conducts the modeling and the hover performance analyses of coaxial co-rotating rotor(or stacked rotor), using a rotorcraft comprehensive analysis code, CAMRAD II. The important design parameters such as the index angle and axial spacing for the coaxial co-rotating rotor are varied in this simulation study. The coaxial co-rotating rotor is trimmed using the torque value of the upper rotor of the previous coaxial counter-rotating rotor or the total thrust value of the previous coaxial counter-rotating rotor in hover. The maximum increases in the rotor thrust is 1.84% for the index angle of -10° when using the torque trim approach. In addition, the maximum decreases in the rotor power is 4.53% for the index angle of 20° with the thrust trim method. Thus, the present study shows that the hover performance of the coaxial co-rotating rotor for e-VTOL aircraft can be changed by the index angle.

• Journal of KIISE:Software and Applications
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• v.26 no.3
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• pp.365-379
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• 1999

본 논문에서는 객체지향 데이터베이스의 클래스 계층에 대한 색인기법으로 이차원 색인구조를 이용하여 조율 가능한 이차원 클래스 색인기법인 2D-CHI를 제안한다. 2D-CHI 에서는 색인된 속성의 키값 도메인과 클래스 식별자 도메인으로 구성된 이차원 도메인상의 색인엔트리들에 대한 클러스터링 문제를 다룬다. 클러스터링 특성이 하나의 속성에 의해서 독점되는 B+-Tree 와 같은 일차원 색인구조를 이용하는 기존의 클래스 색인기법들은 특정 형태의 질의에 대해서만 적합한 색인기법들로서 다양한 형태의 질의들로 구성된 질의 패턴에 대해서 적절하게 대응하지 못한다. 2D-CHI에서는 질의 피턴에 따라 키값 도메인과 클래스 식별자 도메인 사이에서 색이 엔트리들의 클러스터링 정도를 조정함으로써 질의처리의 성능을 향상시킨다. 2D-CHI 의 성능평가를 위하여, 먼저 데이터의 균일 분포를 가정으로 비용 모델을 정립하여 기존의 색인기법들과 색인의 성능을 비교한다. 그리고, 계층 그리드 파일을 이용하여 구현한 2D-CHI의 실험으로 비용 모델을 검증하며, 다양한 실험을 통하여 데이터의 분포와 주어진 질의 형태에 따라 최적의 이차원 클래스 계층 색인구조를 구성할 수 있음을 보인다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.11
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• pp.1209-1216
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• 2009

This paper presents an efficient Quantized Equal Gain Transmission(QEGT) codebook index searching technique for MISO beamforming system in a Rayleigh flat fading channel. The searching time for the optimum weight vector among the codebook vectors increases exponentially when the codebook size increases linearly, although the bit error rate decreases. So, newly defined simple metric is proposed for fast searching, which determines a few candidates. Then the conventional method combined with accurate search algorithm selects the optimal index. This strategy significantly reduces the overall search time, while maintaining almost the same bit error rate performance. Furthermore, as the codebook size increases, the search time is considerably decreased compared to that of the conventional approach.