• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.65-84
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• 2002

The Observational Method proposed by Terzaghi can be applied for the safe and economic construction projects where the exact prediction of the behavior of the structures is difficult as in the underground excavation. The method consists of measuring lateral displacement, ground settlement and axial force of supports in the earlier stage of the construction and back analysis technique to find the best fit design parameters such as earth pressure coefficient, subgrade reaction etc, which will minimize the gap between calculated displacement and measured displacement. With the results, more reliable prediction of the later stage can be obtained. In this study, back analysis programs using the Direct Method, based on the Hill Climbing Method were made and evaluated, and to overcome the limits of the method, Genetic Algorithm(GA) was applied and tested for the actual construction cases.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.479-484
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• 2000

The reliable estimation of the system parameters and the accurate prediction of the system behavior are important to design tunnel safely and economically. Therefore, the back analysis using the field measurements data is useful to evaluate the geotechnical parameter for tunnel. In the back analysis method, the selection of initial value and uncertainty of field measurements influence significantly on the analysis result. In this paper, to overcome uncertainty of field measurements, we performed the back analysis using the displacement data gained at each step of excavation and support.

• Physical Therapy Korea
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• v.16 no.3
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• pp.60-68
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• 2009

Psychsocial factors appear to play an important role in the maintenance and development of chronic disability from low back pain. Fear of pain may be more disabling than the pain itself in patients with nonacute low back pain. The purpose of this study was to identify the contribution of gender, age, depression and pain-related fear to pain intensity and disability in nonacute low back pain patients. This was a cross-sectional survey study of eighty four patients who had low back pain for at least 4 weeks. More than moderate correlations were found between pain intensity, disability, fear-avoidance beliefs and depression. Regression analyses revealed that disability ratings and fear-avoidance beliefs for work activities significantly contributed to the prediction of pain intensity, even when controlling for age, gender and pain duration. Also, fear-avoidance beliefs for physical activity, pain intensity, age and depression, significantly contributed to the prediction of disability, even when controlling for gender and pain duration. These findings suggest that disability scores and fear-avoidance beliefs for work activities are important determinants of pain intensity. They also suggest that fear-avoidance beliefs for physical activity, pain intensity, age and depression are important determinants of disability.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.524-524
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• 2000

We have been developing a method to build one-step-ahead prediction models for time series using genetic programming (GP). Our model building method consists of two stages. In the first stage, functional forms of the models are inherited from their parent models through crossover operation of GP. In the second stage, the parameters of the newborn model arc optimized based on an iterative method just like the back propagation. The proposed method has been applied to various kinds of time series problems. An application to the seismic ground motion was presented in the KACC'99, and since then the method has been improved in many aspects, for example, additions of new node functions, improvements of the node functions, and new exploitations of many kinds of mutation operators. The new ideas and trials enhance the ability to generate effective and complicated models and reduce CPU time. Today, we will present a couple of financial applications, espc:cially focusing on gold price prediction in Tokyo market.

• The Korean Journal of Pain
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• v.33 no.2
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• pp.153-165
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• 2020

Background: Well-validated risk prediction models help to identify individuals at high risk of diseases and suggest preventive measures. A recent systematic review reported lack of validated prediction models for low back pain (LBP). We aimed to develop prediction models to estimate the 8-year risk of developing LBP and its recurrence. Methods: A population based prospective cohort study using data from 435,968 participants in the National Health Insurance Service-National Sample Cohort enrolled from 2002 to 2010. We used Cox proportional hazards models. Results: During median follow-up period of 8.4 years, there were 143,396 (32.9%) first onset LBP cases. The prediction model of first onset consisted of age, sex, income grade, alcohol consumption, physical exercise, body mass index (BMI), total cholesterol, blood pressure, and medical history of diseases. The model of 5-year recurrence risk was comprised of age, sex, income grade, BMI, length of prescription, and medical history of diseases. The Harrell's C-statistic was 0.812 (95% confidence interval [CI], 0.804-0.820) and 0.916 (95% CI, 0.907-0.924) in validation cohorts of LBP onset and recurrence models, respectively. Age, disc degeneration, and sex conferred the highest risk points for onset, whereas age, spondylolisthesis, and disc degeneration conferred the highest risk for recurrence. Conclusions: LBP risk prediction models and simplified risk scores have been developed and validated using data from general medical practice. This study also offers an opportunity for external validation and updating of the models by incorporating other risk predictors in other settings, especially in this era of precision medicine.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2104-2125
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• 2021

This paper presents the verification and validation (V&V) of a calculation module for isotope inventory prediction to control the back-end cycle of spent nuclear fuel (SNF). The calculation method presented herein was implemented in a two-step code system of a lattice code STREAM and a nodal diffusion code RAST-K. STREAM generates a cross section and provides the number density information using branch/history depletion branch calculations, whereas RAST-K supplies the power history and three history indices (boron concentration, moderator temperature, and fuel temperature). As its primary feature, this method can directly consider three-dimensional core simulation conditions using history indices of the operating conditions. Therefore, this method reduces the computation time by avoiding a recalculation of the fuel depletion. The module for isotope inventory calculates the number densities using the Lagrange interpolation method and power history correction factors, which are applied to correct the effects of the decay and fission products generated at different power levels. To assess the reliability of the developed code system for back-end cycle analysis, validation study was performed with 58 measured samples of pressurized water reactor (PWR) SNF, and code-to-code comparison was conducted with STREAM-SNF, HELIOS-1.6 and SCALE 5.1. The V&V results presented that the developed code system can provide reasonable results with comparable confidence intervals. As a result, this paper successfully demonstrates that the isotope inventory prediction code system can be used for spent nuclear fuel analysis.

• Investigative Magnetic Resonance Imaging
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• v.22 no.1
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• pp.18-25
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• 2018

Purpose: To investigate the various imaging factors associated with aggravation of lumbar disc herniation (LDH) and develop a scoring system for prediction of LDH aggravation. Materials and Methods: From 2015 to 2017, we retrospectively reviewed the magnetic resonance imaging (MRI) findings of 60 patients (30 patients with aggravated LDH and 30 patients without any altered LDH). Imaging factors for MRI evaluation included the level of LDH, disc degeneration, back muscle atrophy, facet joint degeneration, ligamentum flavum thickness and interspinous ligament degeneration. Flexion-extension difference was measured with simple radiography. The scoring system was analyzed using receiver operating characteristic (ROC) analysis. Results: The aggravated group manifested a higher grade of disc degeneration, back muscle atrophy and facet degeneration than the control group. The ligamentum flavum thickness in the aggravated group was thicker than in the group with unaltered LDH. The summation score was defined as the sum of the grade of disc degeneration, back muscle atrophy and facet joint degeneration. The area under the ROC curve showing the threshold value of the summation score for prediction of aggravation of LDH was 0.832 and the threshold value corresponded to 6.5. Conclusion: Disc degeneration, facet degeneration, back muscle atrophy and ligamentum flavum thickness are important factors in predicting aggravation of LDH and may facilitate the determination of treatment strategy in patients with LDH. The summation score is available as supplemental data.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1278-1289
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• 2008

Currently in increasing number of urban tunnels with small overburden are excavated according to the principle of the New Austrian Tunneling Method (NATM). Successful design, construction and maintenance of NATM tunnel in urban area demands prediction, control and monitoring of surface settlement, gradient and ground displacement with high accuracy. Use of measured displacement for parameter determination has been researched over the years, and one geotechnical engineering principle has been formed as back analysis. In this paper, back analysis of a ground deformational behavior involving nonlinear behavior is discussed. It is of primary importance to make reliable prediction of deformational behavior for shallow tunnels in soft ground. However, predictions made often prove to be incorrect due to complexity of constitutive law and other relevant factors. Back analysis therefore becomes more important, for it may be used to interpret measured displacement to derive nonlinear material characteristics. The paper shows some example in which a deformational mechanism is studied in the light of inhomogeneous distrubution of Young's module, from which a logic is derived to identify two different types of nonlinear constitutive relationships.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.256-262
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• 2004

Conventionally, profile non-uniformity has been characterized by relying on approximated profile with angle or anisotropy. In this study, a new non-uniformity model for etch profile is presented by applying a discrete wavelet to the image obtained from a scanning electron microscopy (SEM). Prediction models for wavelet-transformed data are then constructed using a back-propagation neural network. The proposed method was applied to the data collected from the etching of tungsten material. Additionally, 7 experiments were conducted to obtain test data. Model performance was evaluated in terms of the average prediction accuracy (APA) and the best prediction accuracy (BPA). To take into account randomness in initial weights, two hundred models were generated for a given set of training factors. Behaviors of the APA and BPA were investigated as a function of training factors, including training tolerance, hidden neuron, initial weight distribution, and two slopes for bipolar sig-moid and linear function. For all variations in training factors, the APA was not consistent with the BPA. The prediction accuracy was optimized using three approaches, the best model based approach, the average model based approach and the combined model based approach. Despite the largest APA of the first approach, its BPA was smallest compared to the other two approaches.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.1
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• pp.66-70
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• 1997

In this paper a new learning algorithm for curvature smoothing and improved generalization for multi-layer neural networks is proposed. To enhance the generalization ability a constraint term of hidden neuron activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. When the total cost consisted of the output error and hidden error is minimized by gradient-descent methods, the additional descent term gives not only the Hebbian learning but also the synaptic weight decay. Therefore it incorporates error back-propagation, Hebbian, and weight decay, and additional computational requirements to the standard error back-propagation is negligible. From the computer simulation of the time series prediction with Santafe competition data it is shown that the proposed learning algorithm gives much better generalization performance.