• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.247-252
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• 2006

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.281-286
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• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Journal of Multimedia Information System
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• v.3 no.4
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• pp.119-128
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• 2016

This study aims to suggest a new algorithm for detecting two-wheelers on road that have various shapes according to the viewing angle for vision based intelligent vehicles. This article describes a new approach to two-wheelers detection algorithm riding on people based on modified Histogram of Oriented Gradients (HOG) using correlation coefficient (CC). The CC between two local area variables, in which one is the person riding a bike and other is its background, can represent correlation relation. First, we extract edge vectors using HOG which includes gradient information and differential magnitude as cell based. And then, the value, which is calculated by the CC between the area of each cell and one of two-wheelers, can be extracted as the weighting factor in process for normalizing the modified HOG cell. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.32 no.10
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• pp.784-790
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• 2008

The feasibility and the accuracy of the correlation coefficient(CC) method for the determination of particle positions along the optical axis in digital particle holography were verified by alidation experiments. A traverse system with capable of high precision was used to move the particle objects by exact known distances between several different positions. The particle positions along the optical axis were calculated by the CC method and compared with their exact values to obtain the errors of the focal plane determination. The tested particles were 2D dots in a calibration target along with different sized glass beads and droplets that reflected and caused a three-dimensional effect. The results show that the CC method can work well for both the 2D dots and the 3D particles. The effect of other particles on the focal plane determination was also investigated. The CC method can locate the focal plane of particles with a high precision, regardless of the existence of other particles.

• Journal of radiological science and technology
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• v.40 no.3
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• pp.415-421
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• 2017

In the case of radiation therapy for prostate cancer, a balloon infused with a certain amount of air through the anus is used to reduce rectal dose. Because of the reason, radiation therapy for prostate cancer has acquired CBCT for daily image induction. In order to maintain the anatomical structure most similar to the first CT taken before treatment, it is pretreated, but it can not be said to be perfectly consistent. In two actual treatment regimens, the volume of the bladder was measured as 45.82 cc and 63.43 cc, and the equivalent diameter was 4.4 cm and 4.9 cm. As a result of this study, the mean volume of the bladder was estimated to be 56.2 cc, 105.6 cc by 20 CBCT. The mean dose of CBCT was 1.74% and the mean Bladder mean dose was 96.67%. In case B, PTV mean dose was 4.31%, Bladder mean Dose was estimated to be 97.35%. The changes in the volume of the bladder resulted in changes in the dose of PTV and bladder. The correlation coefficient of bladder dose according to the change of bladder volume showed linearity of mean dose $R^2=-0.94$. The correlation coefficient of the PTV dose according to the volume change of the bladder showed linearity of mean dose $R^2=0.04$. It was found that the dose change of PTV was larger than that of bladder according to the change of bladder volume.

• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.79-84
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• 2022

This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

• Proceedings of the Korean Geotechical Society Conference
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• 1991.10a
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• pp.332-347
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• 1991

Accurate prediction of future settlement is essential for the settlement control of soft soil by pre-loading method. To predict future settlement in clayey soft soils, several methods like Asaoka method, Hyperbolic Method and Hoshino method are currently being used. These methods predict the future sett1ement by mathmatical treatment of the measured settlement data on the basis of consolidtion theory and empiricism. But the correlation coefficient between the measured and the predicted settlement was relatively low (0.8~0.9). Also, the prediction of future settlemet for the design load is very difficult. In this article, the measured field settlement data was treated as the the field consolidation test. Hence, condolidation coefficient(Cv) and compression index(Cc) was evaluated from the field settlement data. Cv and Cc values from field data was used to calculate the degree of consolidation and settlement at desired time. By this method, the correlation coefficent between the measured and the predicted settlement was significantly increased(0.97~0.99). Also the settlement by the design load after the improvement of soft soil could be predicted reasonably. This method is quite rational and sound but it requires thousands of calculation steps. Today, by the aid of low priced personal computers above mentioned technique could be used much acre economically and effectively than conventional methods. This article presented the mechanisms and capacities of this method and demonstrated the enhanced correlation coefficient when applied to actual field settlement data.

• Smart Structures and Systems
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• v.11 no.4
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• pp.349-364
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• 2013

Recently, researchers in the field of structural health monitoring (SHM) have been rigorously striving to replace the conventional NDE techniques with the smart material based SHM techniques, employing smart materials such as piezoelectric materials. For instance, the electromechanical impedance (EMI) technique employing piezo-impedance (lead zirconate titanate, PZT) transducer is known for its sensitivity in detecting local damage. For practical applications, various external factors such as fluctuations of temperature and loading, affecting the effectiveness of the EMI technique ought to be understood and compensated. This paper aims at investigating the damage monitoring capability of EMI technique in the presence of axial stress with fixed boundary condition. A compensation technique using effective frequency shift (EFS) by cross-correlation analysis was incorporated to compensate the effect of loading and boundary stiffening. Experimental tests were conducted by inducing damages on lab-sized aluminium beams in the presence of tensile and compressive forces. Two types of damages, crack propagation and bolts loosening were simulated. With EFS for compensation, both cross-correlation coefficient (CC) index and reduction in peak frequency were found to be efficient in characterizing damages in the presence of varying axial loading.

• Korean Journal of Optics and Photonics
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• v.14 no.5
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• pp.521-529
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• 2003

For complex-coupled (CC) DFB lasers, we found that there might be little correlation between the single mode yields at threshold and above threshold. At threshold, the single mode yield considering f number of in-phase (IP) CC DFB lasers is the same as that of anti-phase (AP) CC DFB lasers. However, the single mode yield as a function of injection current above threshold of IP CC DFB lasers is much different from that of AP CC DFB lasers. In the case of IP CC DFB lasers, the single mode yield increases as the coupling coefficient ratio (CR) increases, while, in the case of AP CC DFB lasers, the single mode yield decreases rapidly regardless of CR as the injection current increases. In the case of AR-HR combinations, the effect of AR ref1ectivity on the single mode yield increases as the coupling strength decreases. As the coupling strength decreases, the CR at which the increase rate of the single mode yield starts to decrease, increases, and the maximum single mode yield increases. Single mode yields of AR-HR and AR-AR combinations are larger than those of AR-CL and CL-CL combinations.