• Korean Journal of Clinical Pharmacy
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• v.21 no.4
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• pp.364-375
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• 2011

Given that single blockade with angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) can achieve only partial and undurable suppression of the Renin Angiotensin System (RAS), it has been hypothesized that dual blockage would be more beneficial in the management of blood pressure (BP) reduction and prevention of progressive chronic kidney disease (CKD) than either agent alone. Thus, it has been suggested that the combination of an ACEI and an ARB might provide renal benefits to hypertensive patients over and above BP reduction. However, this might also expose patients to additive or synergistic side effects. We attempted to conduct a systematic review to evaluate the benefits and harms of combination therapy in hypertensive patients with or without kidney diseases. MEDLINE and KoreaMed were searched for relevant randomized clinical trials in adult hypertensive patients with or without diabetes (restricted to 1997, limited to trials published in English). Results were summarized using the random-effects model, and between-studies heterogeneity was estimated with $I^2$. A final analysis of ten trials (23,928 patients) revealed that the combination of an ACEI and an ARB reduced blood pressure (SBP/DBP) by 3.95/2.02 mmHg (95% confidence interval [CI], -4.38 to -3.53/-2.33 to -1.71) compared with ACEI monotherapy, and 2.83/2.64 mmHg (95% CI, -3.25 to -2.41/-4.95 to -0.33) compared with ARB monotherapy. Eight trials (391 patients) demonstrated a significant reduction in 24h-proteinuria (weighted mean difference, 0.16 g/day, 95% CI, -0.26-0.05), but they did not translate into an improvement in GFR. Tests for heterogeneity showed no difference in effect among the studies. The combination therapy reduced proteinuria by 30% (95% CI, 23% to 37%) and 39% (95% CI, 31% to 48%) compared with ACEI monotherapy and ARB monotherapy, respectively. However, in patients who had proteinuria more than 0.5 g/day, the combination therapy failed to show significant reduction in urinary protein excretion. The current cumulative evidence suggests that diabetic patients with proteinuria on dual RAS blockade have an increase risk of adverse events such as hyperkalemia, hypotension, and so on, compared with ACEI or ARB alone. It is, therefore, proposed that the combination therapy should not be routinely used for the treatment of hypertension with or without compelling indications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2720-2736
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• 2013

Multi-view face detection has become an active area for research in the last few years. In this paper, a novel multi-view human face detection algorithm based on improved real Adaboost is presented. Real Adaboost algorithm is improved by weighted combination of weak classifiers and the approximately best combination coefficients are obtained. After that, we proved that the function of sample weight adjusting method and weak classifier training method is to guarantee the independence of weak classifiers. A coarse-to-fine hierarchical face detector combining the high efficiency of Haar feature with pose estimation phase based on our real Adaboost algorithm is proposed. This algorithm reduces training time cost greatly compared with classical real Adaboost algorithm. In addition, it speeds up strong classifier converging and reduces the number of weak classifiers. For frontal face detection, the experiments on MIT+CMU frontal face test set result a 96.4% correct rate with 528 false alarms; for multi-view face in real time test set result a 94.7 % correct rate. The experimental results verified the effectiveness of the proposed approach.

• Journal of Korean Society of Rural Planning
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• v.10 no.3 s.24
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• pp.41-51
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• 2004

This study aims to develop a potential evaluation method for urban spatial expansion using remote sensing (RS) and geographic information system (GIS). A multi-criteria evaluation method with several criteria and their weighting values was introduced to evaluate the score and quantification of the potential surface around the existing cities. The six criteria with one geographic factor, slope, and five accessibility factors, time distance from center of the city, national road, interchange of expressway, a big city, and station, were defined for the potential. RS techniques were applied for classification of the actual urban expansion maps between two periods, and GIS functions were used for score of accessibility criteria with a distance decay function from geographic, road and several point maps, which was developed in this study. The new methodology was applied to a test area, Suwon, between 1986 and 1996. In order to optimize the six weighting values, this study made new findings to search the optimal combination of the weighting values from new methodology, weighted scenario method for intensity order (WSM), combined with intensity order and AHP method, including a trial and error method for sensitivity analysis to make the intensity order. The optimal combination of the weighting values by the new method generated the optimal potential surface, considering spatial trend of urban expansion in the test area.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.667-675
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• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.42-53
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• 2012

In pattern recognition and machine learning society, classification has been a classical problem and the most widely researched area. Adaptive boosting also known as Adaboost has been successfully applied to binary classification problems. It is a kind of boosting algorithm capable of constructing a strong classifier through a weighted combination of weak classifiers. On the other hand, the PCA and LDA algorithms are the most popular linear feature extraction methods used mainly for dimensionality reduction. In this paper, the combination of Adaboost and feature extraction methods is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each projection vector is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI dataset and FRGC dataset and showed better recognition rates than sequential application of feature extraction and classification methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.267-273
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• 2015

For newly incoming technologies owing to the advancements in information and communications technology, the new form of information occurs due to the combination form of personal information. In turn, personal information which is combination with existing personal information is on the increase. It is difficult to equalize the method in order to analysis the degree of risk for personal information because it is qualitative method which is defined on the current Personal Information Protection Law. This dissertation presents the model to assess the degree of risk by using the IPA(Importance-Performance Analysis) after measuring the importance and the weighted value for the personal information based on the existing the method of risk assessment. Through the model suggested in this dissertation, the subjective judgement can be excluded, the combination of personal information can be assessed and the standard criteria which is used as the objective indicators from the quantitative degree of risk can be suggested.

• Journal of the Korea Society for Simulation
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• v.28 no.3
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• pp.25-40
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• 2019

Today, container terminals are fiercely competing to attract an increasing number of containers. As a way to improve terminal productivity, this study proposes two dispatching rules for yard truck allocation priorities. First, Multi-Attribute Dispatching Rule(MADR) is an allocation method to calculate the weighted sum of multiple factors affecting container terminal productivity and priority them. Especially, the workload of the quay crane was considered one of the factors to reduce the residence time of the ship. Second, Cycling Dispatching Rule(CDR) is the effective way to increase the number of double cycles that directly affect terminal productivity. To identify the effects of combinations of pooling and dispatching, a comparative experiments was performed on 8 scenarios that combined them. A simulation environment has been developed for experiments and the results have demonstrated that the combination of terminal level pooling and Multi-attribute Dispatching could be an excellent combination in KPIs consisting of GCR and delayed departure of ships, etc.

• Journal of Korean Society of Steel Construction
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• v.20 no.1
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• pp.21-31
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• 2008

response surface method (RSM) is widely used to evaluate th e extremely smal probability of ocurence or toanalyze the reliability of very complicated structures. Althoug h Monte-Carlo Simulation (MCS) technique can evaluate any system, the procesing time of MCS dependson the reciprocal num ber of the probability of failure. The stochastic finite element method could solve thislimitation. However, it is limit ed to the specific program, in which the mean and coeficient o f random variables are programed by a perturbation or by a weigh ted integral method. Therefore, it is not aplicable when erequisite programing. In a few number of stage analyses, RSM can construct a regresion model from the response of the c omplicated structural system, thus, saving time and efort significantly. However, the acuracy of RSM depends on the dist ance of the axial points and on the linearity of the limit stat e functions. To improve the convergence in exact solution regardl es of the linearity limit of state functions, an improved adaptive response surface method is developed. The analyzed res ults have ben verified using linear and quadratic forms of response surface functions in two examples. As a result, the be st combination of the improved RSM techniques is determined and programed in a numerical code. The developed linear adapti ve weighted response surface method (LAW-RSM) shows the closest converged reliability indices, compared with quadratic form or non-adaptive or non-weighted RSMs.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.775-781
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• 2006

An intelligent optimization method for designing Fractional Order PID(FOPID) controllers based on Particle Swarm Optimization(PSO) is presented in this paper. Fractional calculus can provide novel and higher performance extension for FOPID controllers. However, the difficulties of designing FOPID controllers increase, because FOPID controllers append derivative order and integral order in comparison with traditional PID controllers. To design the parameters of FOPID controllers, the enhanced PSO algorithms is adopted, which guarantee the particle position inside the defined search spaces with momentum factor. The optimization performance target is the weighted combination of ITAE and control input. The numerical realization of FOPID controllers uses the methods of Tustin operator and continued fraction expansion. Experimental results show the proposed design method can design effectively the parameters of FOPID controllers.

• Journal of Auto-vehicle Safety Association
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• v.10 no.2
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• pp.6-12
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• 2018

Optimized section shape of aluminum bumper backbeam for enhancing the front high speed crashworthiness was investigated. Front body analysis model of a convertible vehicle was built up and parameter studies were carried out with changing the inner rib shape and the section thickness distribution. First an inner rib shape displaying most efficient structural performance was selected. Next, for the selected section the effect of section thickness combination was examined. Also, a light weighed backbeam section displaying crash performance over the current design was suggested. Finally RCAR front low speed impact analyses were carried out for the optimized models.