• Journal of Positioning, Navigation, and Timing
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• v.10 no.2
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• pp.121-130
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• 2021

In this paper, we propose a method to improve the positioning accuracy of TOA based indoor positioning system in NLOS environments. TOA based indoor positioning systems have been studied mostly considering LOS environments. However, it is almost impossible to maintain the LOS environments due to obstacles such as people, furniture, walls, and so on. The proposed method in this study compensates the range error caused by the NLOS environments. We confirmed that positioning accuracy of a proposed method is improved than conventional algorithms through simulation and field test.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1603-1623
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• 2020

Multiple Mobile Robots System (MMRS) has shown many attractive features in lots of real-world applications that motivate their rapid and wide diffusion. In MMRS, the Cooperative Localization (CL) is the basis and premise of its high-performance task. However, the statistical characteristics of the system noise should be already known in traditional CL algorithms, which is difficult to satisfy in actual MMRS because of the numerous of disturbances form the complex external environment. So the CL accuracy will be reduced. To solve this problem, an improved Adaptive Active Cooperative Localization (A²CL) algorithm based on information optimization strategy for MMRS is proposed in this manuscript. In this manuscript, an adaptive information fusion algorithm based on the variance component estimation under Extended Kalman filter (VCEKF) method for MMRS is introduced firstly to enhance the robustness and accuracy of information fusion by estimating the covariance matrix of the system noise or observation noise in real time. Besides, to decrease the effect of observation uncertainty on CL accuracy further, an observation optimization strategy based on information theory, the Model Predictive Control (MPC) strategy, is used here to maximize the information amount from observations. And semi-physical simulation experiments were carried out to verity the A²CL algorithm's performance finally. Results proved that the presented A²CL algorithm based on information optimization strategy for MMRS cannot only enhance the CL accuracy effectively but also have good robustness.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7B
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• pp.595-605
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• 2012

DV-Hop is one of the well known range-free localization algorithms. The algorithm works well in case of isotropic network since the sensor and anchor nodes are placed in the entire area. However, it results in large errors in case of anisotropic networks where the hop count between nodes is not linearly proportional to the Euclidean distance between them. Hence, we proposed a novel range-free algorithm for anisotropic networks to improve the localization accuracy. In the paper, the Euclidean distance between anchor node and unknown node is estimated by the average hop distance calculated at each hop count with hop count and distance information between anchor nodes. By estimating the unknown location of nodes with the estimated distance estimated by the average hop distance calculated at each hop, the localization accuracy is improved. Simulation results show that the proposed algorithm has more accuracy than DV-Hop.

• Korean Journal of Radiology
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• v.20 no.1
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• pp.94-101
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• 2019

Objective: To investigate the efficacy of motion-correction algorithm (MCA) in improving coronary artery image quality and measurement accuracy using an anthropomorphic dynamic heart phantom and 256-detector row computed tomography (CT) scanner. Materials and Methods: An anthropomorphic dynamic heart phantom was scanned under a static condition and under heart rate (HR) simulation of 50-120 beats per minute (bpm), and the obtained images were reconstructed using conventional algorithm (CA) and MCA. We compared the subjective image quality of coronary arteries using a four-point scale (1, excellent; 2, good; 3, fair; 4, poor) and measurement accuracy using measurement errors of the minimal luminal diameter (MLD) and minimal luminal area (MLA). Results: Compared with CA, MCA significantly improved the subjective image quality at HRs of 110 bpm (1.3 ± 0.3 vs. 1.9 ± 0.8, p = 0.003) and 120 bpm (1.7 ± 0.7 vs. 2.3 ± 0.6, p = 0.006). The measurement error of MLD significantly decreased on using MCA at 110 bpm (11.7 ± 5.9% vs. 18.4 ± 9.4%, p = 0.013) and 120 bpm (10.0 ± 7.3% vs. 25.0 ± 16.5%, p = 0.013). The measurement error of the MLA was also reduced using MCA at 110 bpm (19.2 ± 28.1% vs. 26.4 ± 21.6%, p = 0.028) and 120 bpm (17.9 ± 17.7% vs. 34.8 ± 19.6%, p = 0.018). Conclusion: Motion-correction algorithm can improve the coronary artery image quality and measurement accuracy at a high HR using an anthropomorphic dynamic heart phantom and 256-detector row CT scanner.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.3
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• pp.373-379
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• 2012

This paper presents a fault location algorithm based on the adaptively estimated value of the local sequence source impedance for faults on a parallel transmission line. This algorithm uses only the local voltage and current signals of a faulted circuit. The remote current signals and the zero-sequence current of the healthy adjacent circuit are calculated by using the current distribution factors together with the local terminal currents of the faulted circuit. The current distribution factors consist of local equivalent source impedance and the others such as fault distance, line impedance and remote equivalent source impedance. It means that the values of the current distribution factors can change according to the operation condition of a power system. Consequently, the accuracy of the fault location algorithm is affected by the two values of equivalent source impedances, one is local source impedance and the other is remote source impedance. Nevertheless, only the local equivalent impedance can be estimated in this paper. A series of test results using EMTP simulation data show the effectiveness of the proposed algorithm. The proposed algorithm is valid for a double-circuit transmission line system where the equivalent source impedance changes continuously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.61-80
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• 2018

Location information of individual nodes is important in the implementation of necessary network functions. While extensive studies focus on localization techniques in 2D space, few approaches have been proposed for 3D positioning, which brings the location closer to the reality with more complex calculation consumptions for high accuracy. In this paper, an effective range-free localization scheme is proposed for 3D space localization, and the sensitivity of parameters is evaluated. Firstly, we present an improved algorithm (MDV-Hop), that the average distance per hop of the anchor nodes is calculated by root-mean-square error (RMSE), and is dynamically corrected in groups with the weighted RMSE based on group hops. For more improvement in accuracy, we expand particle swarm optimization (PSO) of intelligent optimization algorithms to MDV-Hop localization algorithm, called PMDV-hop, in which the parameters (inertia weight and trust coefficient) in PSO are calculated dynamically. Secondly, the effect of various localization parameters affecting the PMDV-hop performance is also present. The simulation results show that PMDV-hop performs better in positioning accuracy with limited energy.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1167-1175
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• 2018

Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.1-7
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• 2016

ADAS (Advanced Driver Assistance Systems) requires not only real-time robust lane detection, both straight and curved, but also predicting upcoming steering direction by detecting the curvature of lanes. In this paper, a curvature lane detection algorithm is proposed to enhance the accuracy and detection rate based on using inverse perspective images and Gaussian Mixture Model (GMM) to segment the lanes from the background under various illumination condition. To increase the speed and accuracy of the lane detection, this paper used template matching, RANSAC and proposed post processing method. Through experiments, it is validated that the proposed algorithm can detect both straight and curved lanes as well as predicting the upcoming direction with 92.95% of detection accuracy and 50fps speed.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.387-388
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• 2006

In this paper, we propose an efficient algorithm based on PCA and LDA combining K-means clustering method, which has better accuracy of face recognition than Eigenface and Fisherface. In this algorithm, PCA is firstly used to reduce the dimensionality of original face image. Secondly, a truncated face image data are sub-clustered by K-means clustering method based on Euclidean distances, and all small subclusters are labeled in sequence. Then LDA method project data into low dimension feature space and group data easier to classify. Finally we use nearest neighborhood method to determine the label of test data. To show the recognition accuracy of the proposed algorithm, we performed several simulations using the Yale and ORL (Olivetti Research Laboratory) database. Simulation results show that proposed method achieves better performance in recognition accuracy.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.8
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• pp.793-798
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• 2009

In this paper, the simulation and optimization of SRH (Surface Relief Hologram) masks for printing LCD gate patterns using TIR (Total Internal Reflection) holographic lithography was investigated. A simulation and optimization algorithm based on SDTA (Scalar Diffraction Theory Analysis) method was developed. The accuracy of the algorithm was compared to that of the RCWA (Rigorous Coupled Wave Analysis) method for estimating the Fresnel diffraction pattern of Cr amplitude masks for the given system geometry. In addition, the results from the optimization algorithm were validated experimentally. It was found that one to the most important conditions for the fabrication of SRH masks is to avoid nonlinear shape distortions of the resulting grating. These distortions can be avoided by designing SRH masks with recorded gratings having small aspect ratios of width versus depth. The optimum gap size between the Cr and SRH masks was found using the optimization algorithm. A printed LCD gate pattern with a minimum line width of $1.5{\mu}m$ exposed using the optimized SRH mask was experimentally demonstrated.