• 동의생리병리학회지
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• 제33권6호
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• pp.303-310
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• 2019

In order to search for the causes of having difficulty with the scientification task of the Korean medicine, the definition and conception of science were reviewed first and then formalization of reasoning scheme and a practical method of scientification were proposed. Science in its definition is meant by foundation of method and system for production of scientific knowledge not by knowledge of science itself. The formation of science is composed of complex processes including not only scientific knowledge but also politicosocial output containing activity of scientist society, spreading of social value and intercommunication. The production of scientific knowledge of Korean medicine is begun from logicality of the differential diagnosis and treatment theory through abductive verification of analogical inference by yinyang and 5 phase theory. For the commensurability between the various heterogenic theories within Korean medicine, the scientific activity of collecting, compiling, analyzing, distributing, and discussing the significant knowledge gained through abductive verification in the experiment and clinical process should be formed broadly. Based on these knowledge database, organization of scientist society with Korean medicine, life science, medical engineering, social expansion and generalization of pattern conception, and then social propagation and contribution for national health should be driven forward.

• Computers and Concrete
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• 제12권2호
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• pp.187-209
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• 2013

This paper presents a study on the prediction of transfer length of 13 mm seven-wire prestressing steel strand in pretensioned prestressed concrete members with rectangular cross-section including several material properties and design and manufacture parameters. To this end, a carefully selected database consisting of 207 different cases coming from 18 different sources spanning a variety of practical transfer length prediction situations was compiled. 16 single input features and 5 combined input features are analyzed. A widely used feedforward neural regression model was considered as a representative of several machine learning methods that have already been used in the engineering field. Classical multiple linear regression was also considered in order to comparatively assess performance and robustness in this context. The results show that the implemented model has good prediction and generalization capacity when it is used on large input data sets of practical interest from the engineering point of view. In particular, a neural model is proposed -using only 4 hidden units and 10 input variables-which significantly reduces in 30% and 60% the errors in transfer length prediction when using standard linear regression or fixed formulas, respectively.

• Journal of Computing Science and Engineering
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• 제10권4호
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• pp.118-127
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• 2016

An improved sample balanced genetic algorithm and Extreme Learning Machine (iSBGA-ELM) was designed for accurate diagnosis of Alzheimer disease (AD) and identification of biomarkers associated with AD in this paper. The proposed AD diagnosis approach uses a set of magnetic resonance imaging scans in Open Access Series of Imaging Studies (OASIS) public database to build an efficient AD classifier. The approach contains two steps: "voxels selection" based on an iSBGA and "AD classification" based on the ELM. In the first step, the proposed iSBGA searches for a robust subset of voxels with promising properties for further AD diagnosis. The robust subset of voxels chosen by iSBGA is then used to build an AD classifier based on the ELM. A robust subset of voxels keeps a high generalization performance of AD classification in various scenarios and highlights the importance of the chosen voxels for AD research. The AD classifier with maximum classification accuracy is created using an optimal subset of robust voxels. It represents the final AD diagnosis approach. Experiments with the proposed iSBGA-ELM using OASIS data set showed an average testing accuracy of 87%. Experiments clearly indicated the proposed iSBGA-ELM was efficient for AD diagnosis. It showed improvements over existing techniques.

• ETRI Journal
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• 제17권1호
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• pp.11-22
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• 1995

This paper proposes a modified error function to improve the error back-propagation (EBP) algorithm for multi-Layer perceptrons (MLPs) which suffers from slow learning speed. It can also suppress over-specialization for training patterns that occurs in an algorithm based on a cross-entropy cost function which markedly reduces learning time. In the similar way as the cross-entropy function, our new function accelerates the learning speed of the EBP algorithm by allowing the output node of the MLP to generate a strong error signal when the output node is far from the desired value. Moreover, it prevents the overspecialization of learning for training patterns by letting the output node, whose value is close to the desired value, generate a weak error signal. In a simulation study to classify handwritten digits in the CEDAR [1] database, the proposed method attained 100% correct classification for the training patterns after only 50 sweeps of learning, while the original EBP attained only 98.8% after 500 sweeps. Also, our method shows mean-squared error of 0.627 for the test patterns, which is superior to the error 0.667 in the cross-entropy method. These results demonstrate that our new method excels others in learning speed as well as in generalization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.785-804
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• 2017

Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Herein we address this problem by proposing a hierarchical cascaded classifier for video face recognition, which is a multi-layer algorithm and accounts for the misclassified samples plus their similar samples. Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2001년도 추계학술대회
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• pp.65-71
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• 2001

With ever-change and increasingly competitive business environments, firms strive to employ a variety of marketing strategies and execution in order to survive in the market. Such effects would be paid off in the right way only when management of the firms perform marketing activities focusing on long term effectiveness, which would drive company profits up and keep them for long. Demands of customers are being changed and varied. And in this circumstance, it become a main issue of management that the company should produce and sell products according to the customer demands. With these trends, each company has been concentrating effects on generalization of product development technique and distinction of service lot customer In this result with the advantage of mass marketing and database marketing have been drawing attentions from company. Also the internet connected around the world completely diminished the limit of time and distance and company have enveloped keen competition out of each nation and continent in the world market. To fulfill these demands of customer, they need a concept of eCRM(Web based Customer Relationship Management), and go from selling products and services, or gathering customer requests, up to the phase of solving customer's problem by real time or Previous action. With the help of Internet, the frequency and speed of the problem solving has improved greatly.

• 대한안전경영과학회지
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• 제3권4호
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• pp.123-133
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• 2001

With ever-change and increasingly competitive business environments, firms strive to employ a variety of marketing strategies and execution in order to survive in the market. Such effects would be paid off in the right way only when management of the firms perform marketing activities focusing on long term effectiveness, which would drive company profits up and keep them for long. Demands of customers are being changed and varied. And in this circumstance, it become a main issue of management that the company should produce and sell products according to the customer demands. With these trends, each company has been concentrating effects on generalization of product development technique and distinction of service for customer. In this result with the advantage of mass marketing and database marketing have been drawing attentions from company. Also the internet connected around the world completely diminished the limit of time and distance and company have enveloped keen competition out of each nation and continent in the world market. To fulfill these demands of customer, they need a concept of eCRM(Web based Customer Relationship Management), and go from selling products and services, or gathering customer requests, up to the phase of solving customer's problem by real time or previous action. With the help of internet, the frequency and speed of the problem solving has improved greatly.

• 한국측량학회지
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• 제10권2호
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• pp.63-71
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• 1992

디지탈 지도 데이타베이스는 다중 축적의 개념을 포함하여 여러 가지 목적을 두고 제작되며 단일 축적으로만 사용하기 위해 Base Map을 구축하는 사례는 극히 보기 드믄 현상이라고 할 수 있다. 따라서 지도의 일반화와 다중표현에 대한 Line의 단순화 처리에 있어서 가장 중요한 문제는 일반화된 그래픽 데이타의 정확도와 인식도를 모두 부여하기 위해 Base Map 상의 정보를 단순화하기 위해 설정하는 허용범위를 디지틸 화일내에서 Feature의 형태에 따라 수정이 가능하도록 하는 것이다. 본 연구에서는 하나의 디지털 화일내에서 다양한 축척상으로 수행되는 Line의 단순화에 대한 여러가지 알고리즘을 고찰하였으며, 지도의 표현상에 변화를 줄 수 있는 선형성 Feature 별로 축척에 따른 규칙을 설정하였다. 수치화된 line 데이타 사이의 상관성을 분석하기 위하여 2가지 변형량을 측정하여 5가지 알고리즘에 대한 평가를 시도하였다. 데이타의 분석결과 Douglas-Peucker 알고리즘이 단순화 후의 변형량에 있어 가장 작은 영향을 받음을 알 수 있었다. 이러한 연구 결과로부터 디지탈 화일을 소축척으로 표현하기 위해 단순화를 실시할 경우 내부적으로 지니고 있어야 하는 기하학적인 항목으로서 그 크기와 변동량에 대한 수치적인 안을 제시함에 따라 지도의 단순화에 대한 가능성을 입증하였다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 추계학술대회
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• pp.733-735
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• 2014

표본기반 초해상도(Super Resolution 이하 SR) 기법은 데이터베이스에 저장된 고해상도 영상의 패치와 저해상도 영상의 패치 사이에 대응관계를 이용하여, 저해상도의 입력영상에 가장 유사한 고해상도 패치를 덧붙여서 고해상도를 구성하는 방식이다. 이러한 방식은 한 장의 영상만으로 고해상도 영상을 얻을 수 있고, 위의 과정을 반복하여 2배 이상의 확대된 영상을 얻을 수 있어서 기존의 고전적 SR의 문제점을 해결할 수 있다. 표본기반 SR의 방법들 중 네이버 임베딩(Neighbor Embedding 이하 NE) 기법의 기본 원리는 지역적 선형 임베딩이라는 매니폴드 학습방법의 개념과 같다. 그러나 네이버 임베딩의 빈약한 일반화 능력으로 인하여 알고리즘의 성능을 크게 저하시킨다. 이유는 국부학습 데이터 집합의 크기가 너무 작아서 NE 알고리즘의 성능을 현저히 저하시킨다. 본 논문에서는 이와 같은 문제점을 해결하기 위해서 일반화 능력이 뛰어난 Support Vector Regression(이하 SVR)기반 개선된 NE를 제안하였다. 저해상도 입력 패치가 주어지면 SVR 기반 개선된 NE를 이용하여 고해상도의 해당 화소 값을 예측하였다. 실험을 통하여 제안된 기법이 기존의 보간법 및 NE 기법 등에 비해 정량적인 척도 및 시각적으로 향상된 결과를 보여 주었다.

• 정보과학회 논문지
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• 제41권9호
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• pp.686-698
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• 2014

최근 스마트폰의 폭발적인 보급, IoT와 클라우드 컴퓨팅 기술의 고도화, 그리고 IoT 디바이스의 보편화로 대용량 스트리밍 센싱데이터가 출현하였다. 또한 이를 기반으로 데이터의 공유와 매쉬업 통해 새로운 데이터의 가치를 창출하기 위한 요구사항의 증대로 대용량 스트리밍 센싱데이터 환경에서 시맨틱웹 기술과의 접목에 관한 연구가 활발히 진행되고 있다. 하지만 데이터의 대용량성 스트리밍성으로 인해 새로운 지식을 도출하기 위한 지식 추론분야에서 많은 이슈들에 직면하고 있다. 이러한 배경하에, 본 논문에서는 IoT 환경에서 발생하는 대용량 스트리밍 센싱데이터를 시맨틱웹 기술로 처리하여 서비스하기 위해 RDFS 규칙기반 병렬추론 기법을 제시한다. 제안된 기법에서는 기존의 규칙추론 알고리즘인 Rete 알고리즘을 하둡프레임워크 맵리듀스를 통해 병렬로 수행하고, 공용 스토리지로서 하둡 데이터베이스인 HBase를 사용하여 데이터를 공유한다. 이를 위한 시스템을 구현하고, 대용량 스트리밍 센싱데이터인 기상청 AWS 관측데이터를 이용하여 제시된 기법에 대한 성능평가를 진행하고, 이를 입증한다.