• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2092-2095
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• 2001

In this paper, the performance of a new alternative form of three-axis attitude estimation algorithm for a rigid body is evaluated via simulation for the situation where the observed vectors are the estimated baselines of a GPS antenna array. This method is derived based on a simple iterative nonlinear least-squares with four elements of quaternion parameter. The representation of quaternion parameters for three-axis attitude of a rigid body is free from singularity problem. The performance of the proposed algorithm is compared with other eight existing methods, such as, Transformation Method (TM), Vector Observation Method (VOM), TRIAD algorithm, two versions of QUaternion ESTimator (QUEST), Singular Value Decomposition (SVD) method, Fast Optimal Attitude Matrix (FOAM), Slower Optimal Matrix Algorithm (SOMA).

• 산업공학
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• 제12권2호
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• pp.205-209
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• 1999

In reverse engineering area, the reconstruction of surfaces from scanned or digitized data is being developed, but geometric model of existing objects is not available in industries. This paper presents the new approach to the reconstruction of surface technique. A proposed methodology finds base geometry and blends surface between them. Each based geometry is divided by tri-angular patches which are compared with their normal vector for face grouping. Each group is categorized analytical surface such as a part of cylinder, sphere and cone, and plane shapes to represent the based geometry surface. And then, each based geometry surface is implemented to the infinitive surface. Infinitive surface's intersections are trimmed by boundary representation model reconstruction. This method has several benefits such as time efficiency and automatic functional modeling system in reverse engineering. Especially, it can be directly applied 3D fax and 3D copier.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1648-1649
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• 2007

This paper proposes a very novel method which makes it possible that state feedback controller can be designed for unknown dynamic system with measurable states. The RLS algorithm is used for the identification of input-output relationship. A virtual state space representation is derived from the relationship and the SVM(Support Vector Machines) makes the relationship between actual states and virtual states. A state feedback controller can be designed based on the virtual system and the SVM makes the controller be with actual states. The results of this paper can give many opportunities that the state feedback control can be applied for unknown dynamic systems

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1996년도 추계학술대회논문집; 한국과학기술회관, 8 Nov. 1996
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• pp.91-97
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• 1996

We have all heard sounds that did not sound "right" while riding in an automobile. Objectionable sounds are difficult to find and understand because the sound field is complex and dynamic in the near field of an automobile. Many different noise sources and transmission paths must be understood before an engineering change can be recommended. This paper reviews the fundamental characterization of sound and chscusses the Sound Intensity measurement technique. Sound intensity measurements locate sources and sinks of acoustic energy. Used with narrowband analysis equipment, acoustic noise sources can be identified. Sound intensity measurements are made -in-situ and do not require specmi anechoic facilities. The measurement results in a vector representation of the near field sound field and can discriminate between multiple sound sources.d sources.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.88.4-88
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• 2001

In this paper, we propose a solver for differential equations, using a multi-layer neural network. The multi-layer neural network is a transformer function originally where the function is differential and the explicit representation has been developed. The learning determines the response of neural networks; however, the response is not equal to the output values. The differential relations are also the response. The differential conditions can be also set as teaching data; therefore, there is a possibility to reach a new solver for the differential equations. Since it is unknown how to define the input data for the neural network solver during long terms, we could not derive the expressions. Recently, the analogue type neural network is known and it transforms any vector to another The "any" must be...

• ETRI Journal
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• 제41권4호
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• pp.483-493
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• 2019

Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand-crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking, according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel-level agreement to the model learned from the detection phase is achieved. Our two-phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.

• Communications for Statistical Applications and Methods
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• 제28권3호
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• pp.295-305
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• 2021

Most graphical representation methods for two-dimensional contingency tables are based on the frequencies, probabilities, association measures, and goodness-of-fit statistics. In this work, a method is proposed to represent the correlation coefficients for each of the two selected levels of the row and column variables. Using the correlation coefficients, one can obtain the vector-matrix that represents the angle corresponding to each cell. Thus, these vectors are represented as a unit circle with angles. This is called a CC plot, which is a correlation plot for a contingency table. When the CC plot is used with other graphical methods as well as statistical models, more advanced analyses including the relationship among the cells of the row or column variables could be derived.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 추계학술발표대회
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• pp.877-880
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• 2020

본 논문에서는 언론사 프레임 분석을 위해 2020 년 2 월 1 일부터 7 개월간 코로나 19 를 언급한 기사 데이터를 수집하여 단어 임베딩을 수행하고, 언론사별 중복단어 행렬로 K-Means Clustering 을 수행하여 군집별로 모인 언론사들의 분포를 살펴본다. 또한, 언론사별 중복되지 않는 유일단어들의 긍정, 부정, 정치적, 경제적 등의 특성에 따라 프레임을 분석하여 파악한다. 이를 통해, 특정 기간동안 코로나 19 관련 기사에서 나타나는 언론사별 프레임을 비교 및 분석하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1400-1418
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• 2020

In the development of commercial promotion, chatbot is known as one of significant skill by application of natural language processing (NLP). Conventional design methods are using bag-of-words model (BOW) alone based on Google database and other online corpus. For one thing, in the bag-of-words model, the vectors are Irrelevant to one another. Even though this method is friendly to discrete features, it is not conducive to the machine to understand continuous statements due to the loss of the connection between words in the encoded word vector. For other thing, existing methods are used to test in state-of-the-art online corpus but it is hard to apply in real applications such as telemarketing data. In this paper, we propose an improved chatbot design way using hybrid bag-of-words model and skip-gram model based on the real telemarketing data. Specifically, we first collect the real data in the telemarketing field and perform data cleaning and data classification on the constructed corpus. Second, the word representation is adopted hybrid bag-of-words model and skip-gram model. The skip-gram model maps synonyms in the vicinity of vector space. The correlation between words is expressed, so the amount of information contained in the word vector is increased, making up for the shortcomings caused by using bag-of-words model alone. Third, we use the term frequency-inverse document frequency (TF-IDF) weighting method to improve the weight of key words, then output the final word expression. At last, the answer is produced using hybrid retrieval model and generate model. The retrieval model can accurately answer questions in the field. The generate model can supplement the question of answering the open domain, in which the answer to the final reply is completed by long-short term memory (LSTM) training and prediction. Experimental results show which the hybrid word vector expression model can improve the accuracy of the response and the whole system can communicate with humans.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권4호
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• pp.268-273
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• 2017

최근 감정 분류 분야에서 딥러닝 인코더 기반의 접근 방법이 활발히 적용되고 있다. 딥러닝 인코더 기반의 접근 방법은 가변 길이 문장을 고정 길이 문서 벡터로 압축하여 표현한다. 하지만 딥러닝 인코더에 흔히 사용되는 구조인 장 단기 기억망(Long Short-Term Memory network) 딥러닝 인코더는 문서가 길어지는 경우, 문서 벡터 표현의 품질이 저하된다고 알려져 있다. 본 논문에서는 효과적인 감정 문서의 분류를 위해, 장 단기 기억망의 출력을 중요도에 따라 가중합하여 문서 벡터 표현을 생성하는 주목방법 기반의 딥러닝 인코더를 사용하는 것을 제안한다. 또한, 주목 방법 기반의 딥러닝 인코더를 문서의 감정 분류 영역에 맞게 수정하는 방법을 제안한다. 제안하는 방법은 윈도우 주목 방법(Window Attention Method)을 적용한 단계와 주목 가중치 재조정(Weight Adjustment) 단계로 구성된다. 윈도우 주목 방법은 한 단어 이상으로 구성된 감정 자질을 효과적으로 인식하기 위해, 윈도우 단위로 가중치를 학습한다. 주목 가중치 재조정에서는 학습된 가중치를 평활화(Smoothing) 한다, 실험 결과, 본 논문에서 제안하는 방법은 정확도 기준으로 89.67%의 성능을 나타내어 장 단기 기억망 인코더보다 높은 성능을 보였다.