• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.349-349
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• 2000

A Clustering Adaptive Fuzzy Logic Controller(CAFLC) is applied to the torque control of a brushless do motor drive. Objective of this system includes elimination of torque ripple due to cogging at low speeds under loads. The CAFLC implemented has advantages of computational simplicity, and self-tuning characteristics. Simulation results showed that the torque ripple and dynamic response of the system using a CAFLC were superior to the model reference adaptive controlled system.

• Journal of KIISE
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• v.43 no.6
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• pp.686-691
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• 2016

In this paper, we introduced the adaptive spectral co-clustering, a spectral clustering for multiview data, especially data with more than three views. In the adaptive spectral co-clustering, the performance is improved by sharing information from diverse views. For the efficiency in information sharing, a co-training approach is adopted. In the co-training step, a set of parameters are estimated to make all views in data maximally independent, and then, information is shared with respect to estimated parameters. This co-training step increases the efficiency of information sharing comparing with ordinary feature concatenation and co-training methods that assume the independence among views. The adaptive spectral co-clustering was evaluated with synthetic dataset and multi lingual document dataset. The experimental results indicated the efficiency of the adaptive spectral co-clustering with the performances in every iterations and similarity matrix generated with information sharing.

• Journal of Information Technology Applications and Management
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• v.17 no.1
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• pp.127-139
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• 2010

Customer behavior pattern discovery is the fundament for conducting customer oriented services and the services management. But, the composition, need, interest and experience of customers may be continuously changing, thereof lead to the difficulty in refining a stable description of their consistent behavior pattern. This paper presented a new method for the behavior pattern discovery from a changing collection of customers. It was originally inspired from the swarm intelligence of ant colony. By the adaptive clustering, some typical behavior patterns which reflect the characteristics of related customer clusters can extracted dynamically and adaptively.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.5
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• pp.199-202
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• 2013

In the cluster mechanism of the wireless sensor network, the network lifetime is affected by how cluster heads are selected. One of the representative clustering mechanisms, the low-energy adaptive clustering hierarchy (LEACH), selects cluster heads periodically, resulting in high energy consumption in cluster reconstruction. On the other hand, the adaptive clustering algorithm via waiting timer (ACAWT) proposes a non-periodic re-clustering mechanism that reconstructs clusters if the remaining energy level of a cluster head reaches a given threshold. In this paper, we propose a re-clustering mechanism that uses multiple remaining node energy levels and does re-clustering when the remaining energy level of a cluster head reaches one level lower. Also, in determining cluster heads, both of the number of neighbor nodes and the remaining energy level are considered so that cluster heads can be more evenly placed. From the simulations based on the Qualnet simulator, we validate that our proposed mechanism outperforms ACAWT in terms of the network lifetime.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.459-462
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• 2004

This paper proposes a genetically inspired adaptive clustering algorithm. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster purity. Chromosome encoding that ensures the correct number of clusters and cluster purity is discussed. The required fitness function is desisted on the basis of modified similarity criteria and genetic operators. These are incorporated into the proposed adaptive clustering algorithm. Experimental results show the efficiency of the clustering algorithm on synthetic data sets and real world data sets.

• Journal of the Korea Society for Simulation
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• v.21 no.4
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• pp.47-56
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• 2012

In this study, we use a parallel simulator PASENS(Parallel SEnsor Network Simulator) to predict power consumption and data reception rate of the hierarchical routing protocols for sensor network - LEACH (Low-Energy Adaptive Clustering Hierarchy), TL-LEACH (Two Level Low-Energy Adaptive Clustering Hierarchy), M-LEACH (Multi hop Low-Energy Adaptive Clustering Hierarchy) and LEACH-C (LEACH-Centralized). According to simulation results, M-LEACH routing protocol shows the highest data reception rate for the wider area, since more sensor nodes are involved in the data transmission. And LEACH-C routing protocol, where the sink node considers the entire node's residual energy and location to determine the cluster head, results in the most efficient energy consumption and in the narrow area needed long life of sensor network.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.247-255
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• 2011

Most existing clustering protocols have been aimed to provide balancing the residual energy of each node and maximizing life-time of wireless sensor networks. In this paper, we present the adaptive clustering strategy related to sink position for clustering protocols in wireless sensor networks. This protocol allows networks topology to be adaptive to the change of the sink position by using symmetrical clustering strategy that restricts the growth of clusters based on depth of the tree. In addition, it also guarantees each cluster the equal life-time, which may be extended compared with the existing clustering protocols. We evaluated the performance of our clustering scheme comparing to LEACH and EEUC, and observe that our protocol is observed to outperform existing protocols in terms of energy consumption and longevity of the network.

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.547-555
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• 2014

This paper presents a method for hand segmentation using depth information, and adaptive threshold by means of histogram analysis and color clustering in HSV color model. We consider hand area as a nearer object to the camera than background on depth information. And the threshold of hand color is adaptively determined by clustering using the matching of color values on the input image with one of the regions of hue histogram. Experimental results demonstrate 95% accuracy rate. Thus, we confirmed that the proposed method is effective for hand segmentation in variations of hand color, scale, rotation, pose, different lightning conditions and any colored background.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.273-276
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• 1996

We propose a new method to extract valid lines from a visual image. Unsupervised clustering method is used to assign each line to any of the line groups according to its orientation. During the low-level image processing we use an adaptive threshold method to reduce human supervision and to automate the processing sequence. To reduce the misclassification rate and to suppress the superiors line support regions at the clustering stage, the adaptive threshold method is consistently applied. Performing principal component analysis on each line support region provides an efficient method of obtaining line equation. Finally we adopt the theory of robust statistics to guarantee the quality of each extracted line and to eliminate the lines of poor quality. We present the experimental results to verify our method. With the proposed method, one can extract the lines according to the internal orientation similarities and integrate the whole process into one adaptive procedure.