• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권9호
• /
• pp.3243-3257
• /
• 2021

Deep neural networks provide excellent performance in pattern recognition, audio classification, and image recognition. It is important that they accurately recognize input data, particularly when they are used in autonomous vehicles or for medical services. In this study, we propose a data correction method for increasing the accuracy of an unknown classifier by modifying the input data without changing the classifier. This method modifies the input data slightly so that the unknown classifier will correctly recognize the input data. It is an ensemble method that has the characteristic of transferability to an unknown classifier by generating corrected data that are correctly recognized by several classifiers that are known in advance. We tested our method using MNIST and CIFAR-10 as experimental data. The experimental results exhibit that the accuracy of the unknown classifier is a 100% correct recognition rate owing to the data correction generated by the proposed method, which minimizes data distortion to maintain the data's recognizability by humans.

• 한국언어정보학회지:언어와정보
• /
• 제9권1호
• /
• pp.69-92
• /
• 2005

Pattern recognition in cognitive agents is based on (i) the uninterpreted input data (e.g. parameter values) provided by the agent's hardware devices and (ii) and interpreted patterns (e.g. templates) provided by the agent's memory. Computationally, the task consists in finding the memory data corresponding best to the input data, for any given input. Once the best fitting memory data have been found, the input is recognized by applying to it the interpretation which happens to be stored with the memorized pattern. This paper presents a fast converging procedure which starts from a few initially recognized items and then analyzes the remainder of the input by systematically checking for items shown by memory to have been related to the initial items in previous encounters. In this way, known patterns are tried first, and only when they have been exhausted, an elementary exploration of the input is commenced. Efficiency is improved further by choosing the candidate to be tested next according to frequency.

• Spatial Information Research
• /
• 제20권1호
• /
• pp.27-38
• /
• 2012

최근 CFD 기술 발전과 보편화를 통해 소규모 지역에서의 수치기상모의 영향평가와 설계반영 기법이 단지계획과 엔지니어링 설계에 반영되는 추세이지만 수치지도에서의 건물이나 지물 높이정보 부재와 그에 따른 입력방법의 부정확성 등으로 인해 수치기상모의 결과의 신뢰성이 저하되어, 산업 현장에서 실험적 연구로만 진행될 뿐 일반 업무로는 확대되고 있지 않는 실정이다. 따라서 본 연구는 현재 대도시를 중심으로 구축되고 있는 항공 LiDAR 기술을 통해 수집된 데이터들로부터 수치기상모의에 필요한 기본 입력 자료를 자동 추출 및 구축하여 수치기상모의에 활용하고자 기존의 수치지도 및 현장조사 자료를 기반으로 하는 수치모의와 비교한 결과, 모의 범위 및 해상도가 증가할수록 초기입력자료(.in)의 생상효율성이 증가하였으며, 자료의 품질 및 해상도 또한 향상된 결과를 얻을 수 있었다. 이를 통해 향후, 도시 재개발로 인해 발생하는 입체적인 도시의 물리적 구조 변화를 항공 LiDAR 데이터를 이용함으로써 환경변화예측을 위한 수치모의에 빠르게 이용할 수 있을 것이다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.680-684
• /
• 2005

This paper presents a data-based stability analysis of a MIMO linear time-invariant discrete-time system, as an extension of the previous results for a SISO system. In the MIMO case, a similar discussion as in the case of a SISO system is also applied, except that an augmented input and output space is considered whose dimension is determined in relation to both the orders of the input and output vectors and the numbers of inputs and outputs. As certain subspaces of the input and output space, both output data space and closed-loop data space are defined, which contain all the behaviors of a system, respectively, with zero input in open-loop and with a control input in closed-loop. Then, we can derive the data-based stability conditions, in which the open-loop stability can be checked by using a data matrix whose column vectors span the output data space and the closed-loop stability can also be checked by using a data matrix whose column vectors span the closed-loop data space.

• 전자공학회논문지S
• /
• 제35S권4호
• /
• pp.58-66
• /
• 1998

In many situations, it is very important to identify a certain unkown system, it from its input-output data. For this purpose, several system modeling algorithms have been suggested heretofore, and studies regarding the fuzzy modeling based on its nonlinearity get underway as well. Generatlly, fuzzy models have the capability of dividing input space into several subspaces, compared to linear ones. But hitherto subggested fuzzy modeling algorithms do not take into consideration the correlations between components of sample input data and address them independently of each other, which results in ineffective partition of input space. Therefore, to solve this problem, this letter proposes a new fuzzy modeling algorithm which partitions the input space more efficiently that conventional methods by taking into consideration correlations between components of sample data. As a way to use correlation and divide the input space, the method of principal component is ued. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2006년도 추계학술대회논문집
• /
• pp.230-234
• /
• 2006

This study review about input data needing for noise mapping through the process for noise mapping to the Cheong-Ju on a middle-small scale city. Typically a technician know a input data in noise mapping but it is difficult to get the data. Even if we get the data, it is not regular type. So it take a long time to work out. This study is presented the guideline to solve this problems and indicate about getting data a scheme. and as it make a comparative study of the Europe case.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
• /
• pp.273-275
• /
• 2006

In this paper, we introduce a new design of fuzzy neural networks using input-output data information of target system. The proposed fuzzy neural networks is constructed by input-output data information and used the center of data distance by HCM clustering to obtain the characteristics of data. A membership function is defined by HCM clustering and is applied input-output dat included each rule to conclusion polynomial functions. We use triangular membership functions and simplified fuzzy inference, linear fuzzy inference, and modified quadratic fuzzy inference in conclusion. In the networks learning, back propagation algorithm of network is used to update the parameters of the network. The proposed model is evaluated with benchmark data.

• 한국농공학회지
• /
• 제37권5호
• /
• pp.35-42
• /
• 1995

his paper is to develop a GIS application model (GISCELWAB) for the spatial simulation of surface runoff from a small watershed. The model was constituted by three submodels : The input data extraction model (GISINDATA) which prepares cell-based input data automatically for a given watershed, the cell water balance model (CELWAB) which calculates the water balance for a cell and simulates surface runoff of watershed simultaneously by the interaction of cells, and the output data management model (GISOUTDISP) which visualize the results of temporal and spatial variation of surface runoff. The input data extraction model was developed to solve the time-consuming problems for the input-data preparation of distributed hydrologic model. The input data for CELWAB can be obtained by extracting ASCII data from a vector map. The output data management model was developed to convert the storage depth and discharge of cells into grid map. This model enables to visualize the spatial formulation process of watershed storage depth and surface runoff wholly with time increment.

• 전자공학회논문지CI
• /
• 제41권6호
• /
• pp.39-46
• /
• 2004

클러스터 분석에 있어 중요한 문제 중의 하나는 주어진 데이터에 내재된 적절한 클러스터의 수를 찾아내는 것이다. 본 논문에서는 이러한 클러스터의 개수를 체계적으로 결정하기 위하여 IRC (Input Representation Coverage) 개념을 새로이 정의하고, 이를 이용하여 주어진 데이터에 적합한 클러스터의 개수를 자동 결정하는 방법을 제시한다. 또한, 이러한 방법의 유용성 및 응용성을 알아보기 위하여 가상 데이터를 가지고 분석 실험을 하였으며, 실험을 통해 데이터에 내재된 실제 클러스터의 개수를 찾아내는 데에 제안된 방법이 매우 유용하게 사용될 수 있음을 보여준다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.101.5-101
• /
• 2001

A linear input/output data-based predictive control with integral action is developed. The control input is obtained directly from the input/output data in a single step. However, the state estimation in subspace identification gives a biased estimate and there is model mismatch when the controller is applied to a nonlinear process. To overcome such difficulties, we add integral action to a linear input/output data-based predictive controller by augmenting the integrated white noise disturbance model and use each of best linear unbiased estimation(BLUE) filter and Kalman filter as a stochastic observer for the unmeasured disturbance. When applied to a continuous styrene polymerization reactor the proposed controller demonstrates.