• 한국건설순환자원학회논문집
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• 제8권1호
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• pp.56-63
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• 2020

The usage of OPC-free alkali activated ground granulated blast furnace slag(GGBS) mortar has been widely studied on the previous years, due to its advantages on sustainability, durability and workability. This paper brings a new view, aiming to classify the best application in situ for each mortar, according to the type and activator content. By this practical implication, more efficiency is achieved on the construction site and consequently less waste of materials. In order to compare the different activators, the following experiments were performed: analysis of compressive strength at 28 days, setting time measured by needles penetration resistance, analysis of total pore volume performed by MIP and permeability assessment by RCPT test. In general, activated GGBS had acceptable performance in all cases compared to OPC, and remarkable improved durability. Following the experimental results, it was confirmed that each activator and different concentrations impose distinct outcome performance to the mortar which allows the classification. It was observed that the activator Ca(OH)2 is the most versatile among the others, even though it has limited compressive strength, being suitable for laying mortar, coating/plaster, adhesive and grouting mortar. Samples activated with NaOH, in turn, presented in general the most similar results compared to OPC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.1-15
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• 2022

The MobileNetV3 is specially designed for mobile devices with limited memory and computing power. To reduce the network parameters and improve the network inference speed, a new lightweight network is proposed based on MobileNetV3. Firstly, to reduce the computation of residual blocks, a partial residual structure is designed by dividing the input feature maps into two parts. The designed partial residual structure is used to replace the residual block in MobileNetV3. Secondly, a dual-path feature extraction structure is designed to further reduce the computation of MobileNetV3. Different convolution kernel sizes are used in the two paths to extract feature maps with different sizes. Besides, a transition layer is also designed for fusing features to reduce the influence of the new structure on accuracy. The CIFAR-100 dataset and Image Net dataset are used to test the performance of the proposed partial residual structure. The ResNet based on the proposed partial residual structure has smaller parameters and FLOPs than the original ResNet. The performance of improved MobileNetV3 is tested on CIFAR-10, CIFAR-100 and ImageNet image classification task dataset. Comparing MobileNetV3, GhostNet and MobileNetV2, the improved MobileNetV3 has smaller parameters and FLOPs. Besides, the improved MobileNetV3 is also tested on CPU and Raspberry Pi. It is faster than other networks

• 보건행정학회지
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• 제13권1호
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• pp.98-115
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• 2003

Concerns about growing health insurance expenditures became a national Issue in 2001 when the National Health Insurance went into a deficit. Increases in spending for ambulatory care shared the largest portion of the problem. Methods and systems to control the spending should be developed and a system to measure case mix of providers is one of core components of the control system. The objectives of this article is to examine the feasibility of applying Korean Diagnosis Related Groups (KDRGs) to classify health insurance claims for ambulatory care and to identify problem areas of the classification. A database of 11,586,270 claims for ambulatory care delivered during January 2002 was obtained for the study, and the final number of claims analyzed was 8,319,494 after KDRG numbers were assigned to the data and records with an error KDRG were excluded from the study. The unit of analysis was a claim and resource use was measured by the sum of charges incurred during a month at a department of a hospital of at a clinic. Within group variance was assessed by th coefficient of variation (CV), and the classification accuracy was evaluated by the variance reduction achieved by the KDRG classification. The analyses were performed on both all and non-outlier data, and on a subset of the database to examine the validity of study results. Data were assigned to 787 KDRGs among 1,244 KDRGs defined in the classification system. For non-outlier data, 77.4％ of KDRGs had a CV of charges from tertiary care hospitals less than 100％ and 95.43％ of KDRGs for data from clinics. The variance reduction achieved by the KDRG classification was 40.80％ for non-outlier claims from tertiary care hospitals, 51.98％ for general hospitals, 40.89% for hospitals, and 54.99% for clinics. Similar results were obtained from the analyses performed on a subset of the study database. The study results indicated that KDRGs developed for a classification of inpatient care could be used for ambulatory care, although there were areas where the classification should be refined. Its power to predict tile resource utilization showed a potential for its application to measure case mix of providers for monitoring and managing delivery of ambulatory care. The issue concerning the quality of diagnostic information contained in insurance claims remains to be improved, and significance of future studies for other classification systems based on visits or episodes is guaranteed.

• 한국도서관정보학회지
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• 제39권2호
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• pp.249-266
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• 2008

이 연구는 KDC 제4판 화학공학분야(570)의 주요항목들을 개선하기 위한 것이다. 이를 위해 DDC와 NDC 등 기존의 주요 분류표와 학술진흥재단의 연구분야 분류표 등에 대한 비교분석을 실시하였다. 분석결과 KDC 제4판의 화학공학분야는 현대적인 용어의 사용 및 적절한 분류용어의 선택, 세부주제의 추가전개 등의 개선이 필요한 것으로 나타났다. 이 연구에서는 이러한 문제들을 해결하기 위한 구체적인 개선방안을 제시하였다.

• 한국지능시스템학회논문지
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• 제23권2호
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• pp.178-183
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• 2013

In this study, we proposed a method for electroencephalogram (EEG) classification using invariant CSP at special channels for improving the accuracy of classification. Based on the naive EEG signals from left and right hand movement experiment, the noises of contaminated data set should be eliminate and the proposed method can deal with the de-noising of data set. The considering data set are collected from the special channels for right and left hand movements around the motor cortex area. The proposed method is based on the fit of the adjusted parameter to decline the affect of invariant parts in raw signals and can increase the classification accuracy. We have run the simulation for hundreds time for each parameter and get averaged value to get the last result for comparison. The experimental results show the accuracy is improved more than the original method, the highest result reach to 89.74%.

• 농업과학연구
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• 제47권3호
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• pp.633-644
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• 2020

Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1^st and 2^nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.1252-1255
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• 2002

In this paper, face recognition algorithm, by using skin color information of HSI color coordinate collected from face images, elliptical mask, fratures of face including eyes, nose and mouth, and geometrical feature vectors of face and facial angles, is proposed. The proposed algorithm improved face region extraction efficacy by using HSI information relatively similar to human's visual system along with color tone information about skin colors of face, elliptical mask and intensity information. Moreover, it improved face recognition efficacy with using feature information of eyes, nose and mouth, and Θ1(ACRED), Θ2(AMRED) and Θ 3(ANRED), which are geometrical face angles of face. In the proposed algorithm, it enables exact face reading by using color tone information, elliptical mask, brightness information and structural characteristic angle together, not like using only brightness information in existing algorithm. Moreover, it uses structural related value of characteristics and certain vectors together for the recognition method.

• 한국컴퓨터정보학회논문지
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• 제10권4호
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• pp.203-211
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• 2005

본 논문에서는 대용량 DB에서 개인을 인식하는 새로운 자동 지문인식 시스템을 제안한다. 시스템은 전처리, 분류, 매칭의 3단계로 구성되는데, 분류단계에서는 방향성 이미지 분포의 통계적인 접근 방법에 기반한 새로운 분류기법을 제안하였고, 정합단계에서는 기존 알고리즘보다 더 빠르고 정확한, 개선된 특징점 후보쌍 추출 알고리즘을 제안하였다. 정확성을 위해 정합 단계에서 세선화된 이미지로부터 지문의 특징점을 추출하고 특징점의 연결정보를 사용한 정합과정을 소개한다. 특징점 정합과정에서 연결정보를 사용하는 것은 간단하지만 정확한 방법이며, 두 지문의 비교단계에서 빠르게 기준 특징점 쌍을 선택하는 문제를 해결해 준다. 알고리즘은 지문의 회전과 이동에 무관하다. 제안한 시스템은 반도체 칩방식 입력장치로부터 획득한 1000개의 지문영상으로 실험하였으며, 실험결과는 제안한 방법이 기존방법보다 오인식율은 줄어들고 정확도는 증가하였음을 보여준다.

• 전자공학회논문지
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• 제50권4호
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• pp.182-188
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• 2013

본 논문에서는 정지하고 있는 배경에서 움직이는 목표물을 식별하기 위해 PDR(pulse doppler radar)을 이용하여 수집한 복소수 신호를 처리하는 복소수 SVM(support vector machine)을 제안한다. SVM은 패턴인식 분야에서 널리 이용되나 분류에 이용되는 특징이 대부분 실수 데이터이다. 제안된 복소수 SVM은 실수 데이터, 허수 데이터 정보와 실수부와 허수부 사이의 교차 정보를 모두 이용하여 이동하는 목표물의 분류를 수행한다. 복소수 SVM을 설계하기 위해 최적화 조건 적용 시 실수축과 허수축에 대한 슬랙변수를 고려하였고, 복소수 데이터에 대한 KKT(Karush-Kuhn-Tucker) 조건을 이용하였다. 또한 복소수 거리를 이용한 RBF(radial basis function)를 커널함수로 적용하였다. 제안된 복소수 SVM의 성능을 평가하기 위해 PDR 센서로 수집된 복소 데이터를 기존의 SVM과 복소수 SVM을 이용하여 분류한 결과 기존의 SVM에 비해 복소수 SVM의 식별결과가 개와 사람 각각 8%, 10% 향상되었다.

• 인터넷정보학회논문지
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• 제22권1호
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• pp.23-30
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• 2021

본 논문은 다수의 오토인코더 모델들을 이용한 잡음에 강인한 이미지 분류 시스템을 제안한다. 딥러닝 기술의 발달로 이미지 분류의 정확도는 점점 높아지고 있다. 하지만 입력 이미지가 잡음에 의해서 오염된 경우에는 이미지 분류 성능이 급격히 저하된다. 이미지에 첨가되는 잡음은 이미지의 생성 및 전송 과정에서 필연적으로 발생할 수밖에 없다. 따라서 실제 환경에서 이미지 분류기가 사용되기 위해서는 잡음에 대한 처리 및 대응이 반드시 필요하다. 한편 오토인코더는 입력값과 출력값이 유사하도록 학습되어지는 인공신경망 모델이다. 입력데이터가 학습데이터와 유사하다면 오토인코더의 출력데이터와 입력데이터 사이의 오차는 작을 것이다. 하지만 입력 데이터가 학습데이터와 유사성이 없다면 오토인코더의 출력데이터와 입력데이터 사이의 오차는 클 것이다. 제안하는 시스템은 오토인코더의 입력데이터와 출력데이터 사이의 관계를 이용한다. 제안하는 시스템의 이미지 분류 절차는 2단계로 구성된다. 1단계에서 분류 가능성이 가장 높은 클래스 2개를 선정하고 이들 클래스의 분류 가능성이 서로 유사하면 2단계에서 추가적인 분류 절차를 거친다. 제안하는 시스템의 성능 분석을 위해 가우시안 잡음으로 오염된 MNIST 데이터셋을 대상으로 분류 정확도를 실험하였다. 실험 결과 잡음 환경에서 제안하는 시스템이 CNN(Convolutional Neural Network) 기반의 분류 기법에 비해 높은 정확도를 나타냄을 확인하였다.