• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.258-268
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• 2012

Wireless Sensor Network(WSN) is constituted by low-cost and low-energy, So the most important issue is that the task of the sensor performs successfully by using less energy. In previous WSN, determination of the header and gathering sensor data solution by header give great affection to the performance of network. In this paper, we propose a Hybrid transmission method which considers the direction of data collections. In the proposed hybrid routing method, all of the sensors determine that transmission the data to the sink node directly or indirectly using the head node depend on the location of the head node in the cluster. The performance is compared with the LEACH(Low Energy Adaptive Clustering Hierarchy) by experimental analysis. The results show that the preposed method can reduce the communication distance and energy consumption by avoiding the detour direction of transmission of the data.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.536-543
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• 2004

In this paper, we proposed a new method of modeling a neuro-fuzzy system using a hybrid clustering algorithm. The initial parameters and the number of clusters of the proposed system are optimally chosen simultaneously with respect to the process of regression, which is a unique characteristics of the proposed system. The proposed algorithm presented in this work improves the overall performance of the proposed a neuro-fuzzy system by choosing a proper number of clusters adaptively according the characteristics of given data. The process of clustering is performed by deciding on the number of classes, which yields the property of convergence of the system. In experiments, the superiority of the proposed neuro-fuzzy system is demonstrated, especially the process of optimizing parameters and clustering of learning speed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3472-3480
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• 2010

The Clustering technology of Energy efficiency wireless sensor network gets the energy efficiency by reducing the number of communication between sensor nodes and sink node. In this paper, First analyzed on the clustering technique of the distributed clustering protocol routing scheme LEACH (Low Energy Adaptive Clustering Hierarchy) and HEED (Hybrid, Energy-Efficient Distributed Clustering Approach), and based on this, new energy-efficient clustering technique is proposed for the cause the maximum delay of dead nodes and to increase the lifetime of the network. In the proposed method, the cluster head is elect the optimal efficiency node based on the residual energy information of each member node and located information between sink node and cluster node, and elected a node in the cluster head since the data transfer process from the data been sent to the sink node to form a network by sending the energy consumption of individual nodes evenly to increase the network's entire life is the purpose of this study. To verify the performance of the proposed method through simulation and compared with existing clustering techniques. As a result, compared to the existing method of the network life cycle is approximately 5-10% improvement could be confirmed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.445-452
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• 2019

In this study, parallel processing codes of k-means clustering algorithm were developed and implemented in a PC-cluster for unsupervised classification of large satellite images. We implemented intra-node code using multicores of CPU (Central Processing Unit) based on OpenMP (Open Multi-Processing), inter-nodes code using a PC-cluster based on message passing interface, and hybrid code using both. The PC-cluster consists of one master node and eight slave nodes, and each node is equipped with eight multicores. Two operating systems, Microsoft Windows and Canonical Ubuntu, were installed in the PC-cluster in turn and tested to compare parallel processing performance. Two multispectral satellite images were tested, which are a medium-capacity LANDSAT 8 OLI (Operational Land Imager) image and a high-capacity Sentinel 2A image. To evaluate the performance of parallel processing, speedup and efficiency were measured. Overall, the speedup was over N / 2 and the efficiency was over 0.5. From the comparison of the two operating systems, the Ubuntu system showed two to three times faster performance. To confirm that the results of the sequential and parallel processing coincide with the other, the center value of each band and the number of classified pixels were compared, and result images were examined by pixel to pixel comparison. It was found that care should be taken to avoid false sharing of OpenMP in intra-node implementation. To process large satellite images in a PC-cluster, code and hardware should be designed to reduce performance degradation caused by file I / O. Also, it was found that performance can differ depending on the operating system installed in a PC-cluster.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1271-1282
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• 2022

The optimal design of Water Distribution System (WDS) is used in various ways according to the purpose set by the user. The optimal design of WDS has various purposes, such as minimizing costs and minimizing energy generated when manufacturing pipes. In this study, based on the Modified Hybrid Vision Correction Algorithm (MHVCA), a cost-optimal design was conducted for various WDSs. We also propose a new evaluation index, Best Rate (BR). BR is an evaluation index developed based on the K-mean Clustering Algorithm. Through BR, a comparison was made on the possibility of searching for the optimal design of each algorithm used in the optimal design of WDS. The results of MHVCA for WDS were compared with Vision Correction Algorithm (VCA) and Hybrid Vision Correction Algorithm (HVCA). MHVCA showed a lower cost design than VCA and HVCA. In addition, MHVCA showed better probability of lower cost designs than VCA and HVCA. MHVCA will be able to show good results when applied to the optimal design of WDS for various purposes as well as the optimal design of WDS for cost minimization applied in this study.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1798-1804
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• 2017

This paper presents an approach of the Gustafson-Kessel (GK) clustering algorithm's performance in fault identification on power transmission lines. The clustering algorithm is incorporated in a scheme that uses hybrid intelligent technique to combine artificial neural network and a fuzzy inference system, known as adaptive neuro-fuzzy inference system (ANFIS). The scheme is used to identify the type of fault that occurs on a power transmission line, either single line to ground, double line, double line to ground or three phase. The scheme is also capable an analyzing the fault location without information on line parameters. The range of error estimation is within 0.10 to 0.85 relative to five values of fault resistances. This paper also presents the performance of the GK clustering algorithm compared to fuzzy clustering means (FCM), which is particularly implemented in structuring a data. Results show that the GK algorithm may be implemented in fault identification on power system transmission and performs better than FCM.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.489-498
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• 2006

Clustering results differ according to the datasets and the performance worsens even while using web documents which are manually processed by an indexer, because although representative clusters for a feature can be obtained by statistical feature selection methods, irrelevant features(i.e., non-obvious features and those appearing in general documents) are not eliminated. Those irrelevant features should be eliminated for improving clustering performance. Therefore, this paper proposes three feature-filtering algorithms which consider feature values per document set, together with distribution, frequency, and weights of features per document set: (l) features filtering algorithm in a document (FFID), (2) features filtering algorithm in a document matrix (FFIM), and (3) a hybrid method combining both FFID and FFIM (HFF). We have tested the clustering performance by feature selection using term frequency and expand co link information, and by feature filtering using the above methods FFID, FFIM, HFF methods. According to the results of our experiments, HFF had the best performance, whereas FFIM performed better than FFID.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.38-43
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• 2011

Cluster-based routing is high energy consumption of cluster head nodes. A recent approach to resolving the problem is the dynamic cluster technique that periodically re-selects cluster head nodes to distribute energy consumption of the sensor nodes. However, the dynamic clustering technique has a problem that repetitive construction of clustering consumes the more energies. This paper proposes a solution to the problems described above from the energy efficiency perspective. The round-robin cluster header(RRCH) technique, which fixes the initially structured cluster and sequentially selects cluster head nodes, is suggested for solving the energy consumption problem regarding repetitive cluster construction. A simulation result were compared with the performances of two of the most widely used conventional techniques, the LEACH(Low Energy Adaptive Clustering Hierarchy) and HEED(Hybrid, Energy Efficient, Distributed Clustering) algorithms, based on energy consumption, remaining energy for each node and uniform distribution. The evaluation confirmed that in terms of energy consumption, the technique proposed in this paper was 26.5% and 20% more efficient than LEACH and HEED, respectively.

• Journal of Korean Society of Transportation
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• v.20 no.6
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• pp.99-113
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• 2002

The multi-class traffic assignment problem is the most typical one of the multi-solution traffic assignment problems and, recently formulation of the models and the solution algorithm have been received a great deal of attention. The useful solution algorithm, however, has not been proposed while formulation of the multi-class traffic assignment could be performed by adopting the variational inequality problem or the fixed point problem. In this research, we developed a hybrid solution algorithm which combines GA algorithm, diagonal algorithm and clustering algorithm for the multi-class traffic assignment formulated as a variational inequality Problem. GA algorithm and clustering algorithm are introduced for the wide area and small cost. We also performed an experiment with toy network(2 link) and tested the characteristics of the suggested algorithm.