• Journal of KIISE:Computer Systems and Theory
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• v.37 no.5
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• pp.292-303
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• 2010

On-chip cache memories play an important role in both performance and energy consumption points of view in resource-constrained embedded systems by filtering many off-chip memory accesses. We propose a 2-level data cache architecture with a low energy-delay product tailored for the embedded systems. The L1 data cache is small and direct-mapped, and employs a write-through policy. In contrast, the L2 data cache is set-associative and adopts a write-back policy. Consequently, the L1 data cache is accessed in one cycle and is able to provide high cache bandwidth while the L2 data cache is effective in reducing global miss rate. To reduce the penalty of high miss rate caused by the small L1 cache and power consumption of address generation, we propose an ECP(Early Cache hit Predictor) scheme. The ECP predicts if the L1 cache has the requested data using both fast address generation and L1 cache hit prediction. To reduce high energy cost of accessing the L2 data cache due to heavy write-through traffic from the write buffer laid between the two cache levels, we propose a one-way write scheme. From our simulation-based experiments using a cycle-accurate simulator and embedded benchmarks, the proposed 2-level data cache architecture shows average 3.6% and 50% improvements in overall system performance and the data cache energy consumption.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.20 no.4
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• pp.284-290
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• 1998

The purpose of this study was to assess the soft tissue changes using twenty skeletal class III malocclusion patients who treated with bimaxillary surgery for the correction of dentofacial deformities. Patients were divided into two groups. One was impaction and advancement of maxilla with mandibular set-back (Group 1), the other was downward and advancement of maxilla with mandibular set-back (Group 2). Preoperative and postoperative one year cephalometric data were analyzed and compared. Results obtained were as follows: 1. The ratio of horizontal changes of soft tissue to hard tissue at Nt to ANS, Ls to UI, Li to LI, sPog to Pog were 1:0.60, 1:0.79, 1:0.47, 1:0.63 in group 1 respectively, and 1:0.59, 1:0.48, 1:0.83, 1:1.09 in group 2 respectively. Soft tissue changes were highly predictable at the upper lip, lower lip, and chin area. 2. The ratio of vertical changes of soft tissue to hard tissue at Nt to ANS, Li to LI were 1:0.72, 1:0.06 in group 1, and others showed no statistically significant difference. 3. The ratio of horizontal changes of Ls to hard tissue movements at LI(h) was 1:-0.82 in group 1 and at UI(h), LI(h) were 1:0.48, 1:0.01 in group 2. These ratios of group 1 were greater than those of group 2. 4. The direction of horizontal change of Li was the same as that of hard tissue change. The ratio of horizontal changes of Li to LI was 1:0.47 in group 1 and others showed no statistically significant difference. 5. The changes of upper lip thickness and length were -1.6mm, -1.4mm in group 1, and -1mm, -2.7mm in group 2. 6. The ratios of thickness of upper lip to ANS, UI, LI were 1:-0.83, 1:-0.37, 1:0.11 in group 1. There was similar trend in group 2, and there were no statistically significant difference. These results suggest that prediction of changes in soft tissue of upper lip, lower lip, and chin were 79%, 47%, and 63% in group 1, and 48%, 83%, and 109% in group 2. There was a tendency to decrease in thickness and increase in length of the upper lip.

• Fire Science and Engineering
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• v.27 no.3
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• pp.85-93
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• 2013

Modern fire fighting jobs have been expanded to include areas of rescue, emergency medical service as well as conventional fire suppression, so that load for fire fighting jobs has been increased. Specifically, musculoskeletal disorders (MSDs) such as low back injury have been considered as one of major industrial hazards in heavy manual material handling during fire fighting jobs. This study tried to evaluate risk levels and to prepare background for reducing risk levels associated with heavy manual material handling during fire fighting jobs. This study applied two major tools in evaluating heavy manual material handling jobs which were NLE (NIOSH Lifting Equation) and 3DSSPP (3D Static Strength Prediction Program). A risk index in terms of heavy manual material handling during fire fighting jobs was identified. This index consisted of seven risk levels ranged from nine points (the first level) to three points (the seventh level). There was no job associated with the first level (the highest risk level) of index. There was only one job (life saving job) belonging to the second level (the second highest risk level) of index. The third level had jobs such as usage of destruction equipment and lifting patient. A total of basic eighteen jobs was categorized into six different levels (2nd-7th levels) of index. The outcome of the study could provide a good basis for conducting job intervention, preparing good equipment and developing good education program in order to prevent and reduce MSDs including low back injury of fire fighting jobs.

• The Korean Journal of Pain
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• v.24 no.2
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• pp.81-86
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• 2011

Background: Facet joint disease plays a major role in axial low-back pain. Few diagnostic tests and imaging methods for identifying this condition exist. Single photon emission computed tomography (SPECT) is reported that it has a high sensitivity and specificity in diagnosing facet disease. We prospectively evaluated the use of bone scintigraphy with SPECT for the identification of patients with low back pain who would benefit from medial branch block. Methods: SPECT was performed on 33 patients clinically suspected of facet joint disease. After SPECT, an ultrasound guided medial branch block was performed on all patients. On 28 SPECT-positive patients, medial branch block was performed based on the SPECT findings. On 5 negative patients, medial branch block was performed based on clinical findings. For one month, we evaluated the patients using the visual analogue scale (VAS) and Oswestry disability index. SigmaStat and paired t-tests were used to analyze patient data and compare results. Results: Of the 33 patients, the ones who showed more than 50% reduction in VAS score were assigned 'responders'. SPECT positive patients showed a better response to medial branch blocks than negative patients, but no changes in the Oswestry disability index were seen. Conclusions: SPECT is a sensitive tool for the identification of facet joint disease and predicting the response to medial branch block.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.61-67
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• 2006

Various methods have been applied for the research to model the relationship between rainfall-runoff, which shows a strong nonlinearity. In particular, most researches to model the relationship between rainfall-runoff using artificial neural networks have used back propagation algorithm (BPA), Levenberg Marquardt (LV) and radial basis function (RBF). and They have been proved to be superior in representing the relationship between input and output showing strong nonlinearity and to be highly adaptable to rapid or significant changes in data. The theory of artificial neural networks is utilized not only for prediction but also for classifying the patterns of data and analyzing the characteristics of the patterns. Thus, the present study applied self?organizing map (SOM) based on Kohonen's network theory in order to classify the patterns of rainfall-runoff process and analyze the patterns. The results from the method proposed in the present study revealed that the method could classify the patterns of rainfall in consideration of irregular changes of temporal and spatial distribution of rainfall. In addition, according to the results from the analysis the patterns between rainfall-runoff, seven patterns of rainfall-runoff relationship with strong nonlinearity were identified by SOM.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.6019-6023
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• 2013

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.236-242
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• 2015

We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.

• Journal of the Korean Geotechnical Society
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• v.25 no.4
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• pp.19-29
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• 2009

In this study, a numerical analysis was performed to predict the sequential behavior of anchored retaining wall where the failure accident took place, and verified accuracy of prediction through the comparisons between prediction and field measurement. The emphasis was given to the wall behaviors and the variation of sliding surface based on the two different methods of elasto-plastic and finite element (shear strength reduction technique). Through the comparison study, it is shown that the bending moment and the soil pressure at construction stages produce quite similar results in both the elasto-plastic and finite element method. However, predicted wall deflections using elasto-plastic method show underestimate results compared with measured deflections. This demonstrates that the elasto-plastic method does not clearly consider the influence of soil-wall-reinforcement interaction, so that the tension force (anchor force and earth pressure) on the wall is overestimated. Based on the results obtained, it is found that finite element method using shear strength reduction method can be effectively used to perform the back calculation analysis in the anchored retaining wall, whereas elasto-plastic method can be applicable to the preliminary design of retaining wall with suitable safety factor.

• Journal of Animal Science and Technology
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• v.65 no.4
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• pp.720-734
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• 2023

In Korea, Korea Proven Bulls (KPN) program has been well-developed. Breeding and evaluation of cows are also an essential factor to increase earnings and genetic gain. This study aimed to evaluate the accuracy of cow breeding value by using three methods (pedigree index [PI], pedigree-based best linear unbiased prediction [PBLUP], and genomic-BLUP [GBLUP]). The reference population (n = 16,971) was used to estimate breeding values for 481 females as a test population. The accuracy of GBLUP was 0.63, 0.66, 0.62 and 0.63 for carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS), respectively. As for the PBLUP method, accuracy of prediction was 0.43 for CWT, 0.45 for EMA, 0.43 for MS, and 0.44 for BFT. Accuracy of PI method was the lowest (0.28 to 0.29 for carcass traits). The increase by approximate 20% in accuracy of GBLUP method than other methods could be because genomic information may explain Mendelian sampling error that pedigree information cannot detect. Bias can cause reducing accuracy of estimated breeding value (EBV) for selected animals. Regression coefficient between true breeding value (TBV) and GBLUP EBV, PBLUP EBV, and PI EBV were 0.78, 0.625, and 0.35, respectively for CWT. This showed that genomic EBV (GEBV) is less biased than PBLUP and PI EBV in this study. In addition, number of effective chromosome segments (M_e) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between M_e and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between M_e and accuracy were -0.74 in CWT, -0.75 in EMA, -0.73 in MS, and -0.75 in BF, which were strongly negative. These results proved that the estimation of genetic ability using genomic data is the most effective, and the smaller the M_e, the higher the accuracy of EBV.

• Geotechnical Engineering
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• v.10 no.4
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• pp.17-28
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• 1994

It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basic of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully. In this study, error back propagation algorithm which is one of the teaching technique of artificial neural networks is applied to predict ultimate bearing capacity of pile foundations. For the verification of applicability of this system, a total of 28 data of model pile test results are used. The 9, 14 and 21 test data respectively out of the total 28 data are used for training the networks, and the others are used for the comparison between the predicted and the measured. The results show that the developed system can provide a good matching with model pile test results by training with data more than 14. These limited results show the possibility of utilizing the neural networks for pile capacity prediction problems.