• Journal of the Korean GEO-environmental Society
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• v.3 no.1
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• pp.57-66
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• 2002

Generally, the California Bearing Ratio(CBR) for the material of subgrade is estimated without considering the anti-frost layer into the subgrade layer when pavements are designed. A pavement structure is determined according to the CBR. However, recently the design method taking the anti-frost layer into the subgrade layer is getting prevail. It makes the top of the subgrade layer strengthen and the thickness of the road pavement structure decreased. By the way, some confusion may be caused because theoretically the general equation for the mean CBR to combine the material of the subgrade layer and anti-frost layer have not been developed well. In this paper, laboratory and field CBR tests were performed to estimate of the mean CBR for the subgrade layer including the anti-frost layer. From the basis of the test results, modified equation which is calculating the mean CBR of the subgrade layer has been proposed. Finally, economical efficiency was considered by comparing the pavement thickness with the road pavement design using CBR of the subgrade layer alone and the road pavement design using the mean CBR including the anti-frost layer.

• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.4
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• pp.89-98
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• 1996

This study was conducted to evaluate the relationships between the geotechnical properties and the CBR values of the subgrade materials used in the rural roal construction. A total of 77 Soil samples was investigated and tested from 45 agricultural and industrial sites in Kyungpook Province. The results obtained are as follows : 1. The maximum dry densities of the coarse grained soils are larger than those of the fine grained soils. The optimum moisture contents of the coarse grained soils are smaller than those of the fine grained scils. 2. The mean values of the medified CBR values of the soils classified by the USCS, are decreased in the order of GP-GM, SW-SM, GM, SC, SP-SM, ML, CL-ML. And, those classified by the AASHTO are decreased in the order of A-i-a, A-i-b, A-2-4, A-3, A-4, A-6, A-7-6. 3. As passing percentage of No.200 sieve is increased, the CBR Value of soils is decreased gradually. 4. As the optimum moisture contents of the soil is increased, the CBR values is decresed the maximum dry density of the soils increased, the CBR values increased. 5. The CBR values are decreased as Group-lndex(GI) are increased. And Activity(A) is showed no relation with the CBR values. 6. The relation ships between the modified CBR value and standand proctor compaction CBR value at 95% compaction ratio can he expressed as the following equation : Y(CERmod)= 2.3638 + 0.8922X(CBR25).

• Geomechanics and Engineering
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• v.33 no.2
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• pp.183-194
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• 2023

This study presents the prediction of the California bearing ratio (CBR) of coarse- and fine-grained soils using artificial intelligence technology. The group method of data handling (GMDH) algorithm, an artificial neural network-based model, was used in the prediction of the CBR values. In the design of the prediction models, various combinations of independent input variables for both coarse- and fine-grained soils have been used. The results obtained from the designed GMDH-type neural networks (GMDH-type NN) were compared with other regression models, such as linear, support vector, and multilayer perception regression methods. The performance of models was evaluated with a regression coefficient (R²), root-mean-square error (RMSE), and mean absolute error (MAE). The results showed that GMDH-type NN algorithm had higher performance than other regression methods in the prediction of CBR value for coarse- and fine-grained soils. The GMDH model had an R² of 0.938, RMSE of 1.87, and MAE of 1.48 for the input variables {G, S, and MDD} in coarse-grained soils. For fine-grained soils, it had an R² of 0.829, RMSE of 3.02, and MAE of 2.40, when using the input variables {LL, PI, MDD, and OMC}. The performance evaluations revealed that the GMDH-type NN models were effective in predicting CBR values of both coarse- and fine-grained soils.

• Geotechnical Engineering
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• v.7 no.4
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• pp.25-34
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• 1991

In this study, relationships between CBR values tested by Korean Standards (KS CBR) and the elastic moduli from CU compression tests are developed for the subgrade soils. Triaxial compression and KS CBR tests are carried out on five types of samples from 15 points in Korean ezpressways. Triaxial compression tests are performed under 3 types of coifining pressures to generalize the CBR -elastic modulus relationship as functions of confining pressured and mean principal stresses. From the regression analyses of experimental results, equations for relationships between the KS CBR and elastic moduli of roadbed Boils are proposed. An equation for the relation- ship between the KS CBR and the maximum dry density of roadbed soil is also proposed.

• Journal of Cerebrovascular and Endovascular Neurosurgery
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• v.26 no.2
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• pp.130-140
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• 2024

Objective: To assess whether local anesthetic infiltration could minimize the carotid baroreceptor reflex (CBR) which has an incidence after carotid artery stenting (CAS) that varies from 29% to 51%. Methods: This retrospective single-center study included 51 patients (mean age, 70.47 years) who underwent CAS for carotid stenosis. The groups included patients who underwent CAS for asymptomatic ischemic stroke (n=41) or symptomatic disease (n=10). Preprocedural percutaneous lidocaine injections (PPLIs) were administered to 70.6% and 5.9% of patients who underwent elective CAS and emergency CAS, respectively. Results: Among patients who received PPLIs, the mean degree of stenosis was 80.5% (95% confidence interval [CI]: ±10.74, 51-98%). The mean distance from the common carotid artery bifurcation to the most stenotic lesion (CSD) was 8.3 mm (95% CI: ±0.97, 6.3-10.2 mm); the mean angle between the internal carotid artery and common carotid artery (CCA) trunk (IAG) was 65.6° (95% CI: ±2.39, 61-70°). Among patients who did not receive PPLIs, the mean degree of stenosis was 84.0% (95% CI: ±8.96, 70-99%). The mean CSD was 5.9 mm (95% CI: ±1.83, 1.9-9.9 mm); the mean IAG was 60.4° (95% CI: ±4.41, 51-70°). The procedure time was longer in the PPLI group than in the no PPLI group (28.19 [n=39] vs. 18.88 [n=12] days) (P=0.057); the length of intensive care unit stay was shorter in the PPLI group (20.01 [n=36] vs. 28.10 [n=5] days) (P=0.132). Conclusions: Targeted PPLI administration to the carotid bulb decreased aberrant heart rates and blood pressure changes induced by carotid stent deployment and balloon inflation. As CBR sensitivity increases with decreasing distance to the stenotic lesion from the CCA bifurcation, PPLIs may help stabilize patients during procedures for stenotic lesions closer to the CCA.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.15-17
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• 1999

사례 기반 추론(Case-Based Reasoning)은 새로운 문제를 해결하기 위해 유사한 기존 문제를 추출하여 그 해결과정을 사용한다. 그러므로, 기존의 문제와의 유사성을 얼마만큼 잘 판별하는가가 매우 중요한 관건이다. 연구된 유사성 판단 방법으로는 퍼지 소속 함수(Fuzzy membership function)를 이용하여 사례마다 각 클래스에 대한 소속 함수 값을 주는 방법이 있다. 이 방법은 퍼지 소속 함수를 어떻게 주는가에 따라 성능이 달라진다. 본 논문에서는 적당한 퍼지 소속 함수를 주기 위하여 Fuzzy C-Means를 사용하는 방법을 제안하였다. 이 방법은 각 클래스에 대한 소속 함수 값을 결정하는데 있어서 좀 더 전체적인 데이터 분포 정보를 이용할 수 있다.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.95-98
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• 1998

In this paper, we propose a cell scheduling algorithm for ABR traffic in ATM multiplexer. Proposed Algorithm can support ABR service more efficiently than existing WRR and DWRR algorithm. We evaluate the performances of proposed algorithm through computer simulation. Also, we model the VBR and the ABR traffics as ON/OFF source, and the CBR traffic as a Poisson source. And the simulation shows that proposed algorithm better performance over other cell scheduling algorithm in tem of mean cell delay time.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.3
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• pp.54-64
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• 1998

In this paper, a cell scheduling algorithm is proposed to satisfy the service requirements of CBR, VBR traffics. Particularly, inthe proposed algorithm an ABR traffic which is not included in the conventional cell schedulaing algorithm is treated as one kind of traffic types. The algorithm of RT-VBR and NRT-VBR traffic such that the service requrements of RT-VBR and NRT-VBR traffic are satisfied. The proposed algorithm dynamically schedules cells in a real time by considerin the current traffic conditions. The simulation of the proposed algorithms such as WRR or DWRR in terms of the mean delay time and the maximum queue length.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3196-3198
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• 1999

In this paper, we are proposed ATM AAL 1 source clock recovery methods for CBR service. The proposed method compute the difference between network clock level and the reference level by inspecting the variation of a buffer. Also it is the service clock recovery method that control local clock using the look-up table defined clock dividing rate of the difference in advance. It can be applicable to both SDH network and PDH network which has no common reference clock between its ends, it has an important mean in view of the internetworking between existing networks for the integrated service chased by B_ISDN.