• Korean Journal of Radiology
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• v.19 no.6
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• pp.1053-1065
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• 2018

Objective: To evaluate the clinical impact of using registration software for ablative margin assessment on pre-radiofrequency ablation (RFA) magnetic resonance imaging (MRI) and post-RFA computed tomography (CT) compared with the conventional side-by-side MR-CT visual comparison. Materials and Methods: In this Institutional Review Board-approved prospective study, 68 patients with 88 hepatocellulcar carcinomas (HCCs) who had undergone pre-RFA MRI were enrolled. Informed consent was obtained from all patients. Pre-RFA MRI and post-RFA CT images were analyzed to evaluate the presence of a sufficient safety margin (${\geq}3mm$) in two separate sessions using either side-by-side visual comparison or non-rigid registration software. Patients with an insufficient ablative margin on either one or both methods underwent additional treatment depending on the technical feasibility and patient's condition. Then, ablative margins were re-assessed using both methods. Local tumor progression (LTP) rates were compared between the sufficient and insufficient margin groups in each method. Results: The two methods showed 14.8% (13/88) discordance in estimating sufficient ablative margins. On registration software-assisted inspection, patients with insufficient ablative margins showed a significantly higher 5-year LTP rate than those with sufficient ablative margins (66.7% vs. 27.0%, p = 0.004). However, classification by visual inspection alone did not reveal a significant difference in 5-year LTP between the two groups (28.6% vs. 30.5%, p = 0.79). Conclusion: Registration software provided better ablative margin assessment than did visual inspection in patients with HCCs who had undergone pre-RFA MRI and post-RFA CT for prediction of LTP after RFA and may provide more precise risk stratification of those who are treated with RFA.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.595-598
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• 2013

Background: Lung cancer is the leading cause of cancer death worldwide Therefore, identification of genetic as well as environmental factors is very important in developing novel methods of lung cancer prevention. However, this is a multi-layered problem. Therefore a lung cancer risk prediction system is here proposed which is easy, cost effective and time saving. Materials and Methods: Initially 400 cancer and non-cancer patients' data were collected from different diagnostic centres, pre-processed and clustered using a K-means clustering algorithm for identifying relevant and non-relevant data. Next significant frequent patterns are discovered using AprioriTid and a decision tree algorithm. Results: Finally using the significant pattern prediction tools for a lung cancer prediction system were developed. This lung cancer risk prediction system should prove helpful in detection of a person's predisposition for lung cancer. Conclusions: Most of people of Bangladesh do not even know they have lung cancer and the majority of cases are diagnosed at late stages when cure is impossible. Therefore early prediction of lung cancer should play a pivotal role in the diagnosis process and for an effective preventive strategy.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• The Journal of Engineering Geology
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• v.23 no.1
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• pp.67-77
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• 2013

We conducted a Tunnel Seismic Prediction (TSP) survey in a spillway tunnel at Juam Dam to predict the locations of major discontinuities ahead of the tunnel face. We compared the results of the TSP survey with those from pre-construction inspections (including a surface resistivity survey and borehole investigations) as well as with direct tunnel-face mapping during excavation. The TSP method predicted the locations of major fracture zones that were unnoticed in the pre-construction inspections. The reinforcement patterns planned on the basis of pre-construction inspections were changed on the basis of the TSP results. The results demonstrate that TSP surveys are a cost-effective and reliably accurate method of predicting the locations of fracture zones. Although the TSP method has some limitations, these results suggest that the method is generally useful for predicting geological conditions prior to tunnel face construction.

• Journal of the Korean Geotechnical Society
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• v.29 no.12
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• pp.105-111
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• 2013

The standard penetration test (SPT) has been widely used because of its usability, economy, and many correlations with soil properties among other factors. In SPT, hammer energy is an important factor to evaluate and calibrate N values. To measure hammer energy, an instrumented SPT rod was developed considering that stress waves transferring on rods during SPT driving are the same as stress waves transferring on piles due to pile driving. Using this idea, an instrumented SPT rod with a pile driving analyzer was applied as a pile capacity prediction tool in this study. In order to evaluate this method, SPT and dynamic cone tests with the instrumented SPT rod were conducted and also 2 pile load tests were performed on pre-bored steel pipe piles at the same test site. End bearings were predicted by CAPWAP analysis on force and velocity waves from dynamic cone penetration tests and SPT. Comparing these predicted end bearings with static pile load tests, a new prediction method of the end bearing capacity using the instrumented SPT rod was proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.822-829
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• 2009

This paper proposes a new method of improving the quality of High Efficiency Advanced Audio Coding (HE-AAC). HE-AAC encodes input source by allocating bits for each scalefactor bands appropriately according to human ear's psychoacoustic property. As a result, insufficient bits are assigned to the bands which have relatively low energy. This imbalance between different energy bands can cause decreasing of sound quality like musical noise. In the proposed system, a Linear Prediction (LP) module is combined with HE-AAC as a pre-processor to improve sound quality by even bits distribution. To apply accurate human being's psychoacoustic property, the psychoacoustic model uses Fast Fourier Transform (FFT) spectrum of original input signal to make masking threshold. In its implementation, masking threshold of psychoacoustic model is normalized using the LP spectral envelope in prior to quantization of the LP residual. Experimental result shows that, the proposed algorithm allocates bits appropriately for insufficient bits condition and improves the performance of HE-AAC.

• Journal of agriculture & life science
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• v.53 no.3
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• pp.147-157
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• 2019

In this study, we tried to find out the most appropriate pre-processing method and to verify the feasibility of developing a low-price sensing system for predicting the hardy kiwis sugar content based on VNIRS and subsequent spectral analysis. A total of 495 hardy kiwi samples were collected from three farms in Muju, Jeollabukdo, South Korea. The samples were scanned with a spectrophotometer in the range of 730-2300 nm with 1 nm spectral sampling interval. The measured data were arbitrarily separated into calibration and validation data for sugar content prediction. Partial least squares (PLS) regression was performed using various combinations of pre-processing methods. When the latent variable (LV) was 8 with the pre-processing combination of standard normal variate (SNV) and orthogonal signal correction (OSC), the highest R2 values of calibration and validation were 0.78 and 0.84, respectively. The possibility of predicting the sugar content of hardy kiwi was also examined at spectral sampling intervals of 6 and 10 nm in the narrower spectral range from 730 nm to 1200 nm for a low-price optical sensing system. The prediction performance had promising results with R2 values of 0.84 and 0.80 for 6 and 10 nm, respectively. Future studies will aim to develop a low-price optical sensing system with a combination of optical components such as photodiodes, light-emitting diodes (LEDs) and/or lamps, and to locate a more reliable prediction model by including meteorological data, soil data, and different varieties of hardy kiwi plants.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.619-628
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• 2007

This study is devoted to suggesting the norm of detection abnormal users who are inferior to the other users in the recommender system compared with estimation accuracy. To select the abnormal users, we propose the pre-filtering method by using the preference ratings to the item rated by users. In this study, the experimental result shows the possibility of detecting the abnormal users before the process of preference estimation through the prediction algorithm. And It will be possible to improve the performance of the recommender system by using this detecting norm.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.04a
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• pp.847-852
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• 2000

This paper presents an evaluation of the behavior and strength of two pre-tensioned concrete deep beams tested to failure with using the nonlinear strut-tie model approach. In the approach, the effective prestressing forces represented be equivalent external loads are gradually introduced along its transfer length in the nearest strut-tie model joints, the friction at the interface of main diagonal shear cracks is modeled by diagonal struts along the direction of the cracks in strut tie-model, and additional positioning of concrete ties a the place of steel ties is incorporated. Through the analysis of pre-tensioned concrete deep beams, the nonlinear strut-tie model approach proved to present effective solutions for prediction the essential aspects of the behavior and strength of pre-tensioned concrete deep beams.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.972-977
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• 2009

For the improvement of a train speed in the conventional line from overhead system point of view, it is necessary to approach not only in the aspect of the increasement of tension for catenary but the application of a pre-sag. A certain tension acting on a dropper is removed when it is slack by the pantograph passing. Right after the pantograph passes, the dynamic force caused by the mass of contact wire acts on the dropper. If the pre-sag is applied to the catenary, the static force on the dropper is increased. For the assurance of the safety for the dropper, it is necessary to predict the dynamic force. The purpose of this study is to predict and analyze the force on the dropper according to the applied pre-sag.