• 제목/요약/키워드: Data fitting algorithm

검색결과 200건 처리시간 0.024초

Application and Research of Monte Carlo Sampling Algorithm in Music Generation

  • MIN, Jun;WANG, Lei;PANG, Junwei;HAN, Huihui;Li, Dongyang;ZHANG, Maoqing;HUANG, Yantai
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제16권10호
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    • pp.3355-3372
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    • 2022
  • Composing music is an inspired yet challenging task, in that the process involves many considerations such as assigning pitches, determining rhythm, and arranging accompaniment. Algorithmic composition aims to develop algorithms for music composition. Recently, algorithmic composition using artificial intelligence technologies received considerable attention. In particular, computational intelligence is widely used and achieves promising results in the creation of music. This paper attempts to provide a survey on the music generation based on the Monte Carlo (MC) algorithm. First, transform the MIDI music format files to digital data. Among these data, use the logistic fitting method to fit the time series, obtain the time distribution regular pattern. Except for time series, the converted data also includes duration, pitch, and velocity. Second, using MC simulation to deal with them summed up their distribution law respectively. The two main control parameters are the value of discrete sampling and standard deviation. Processing the above parameters and converting the data to MIDI file, then compared with the output generated by LSTM neural network, evaluate the music comprehensively.

ASMOD를 이용한 3차원 자유 형상 설계 (3-Dimensional Free Form Design Using an ASMOD)

  • 김현철;김수영;이창호
    • 한국지능시스템학회논문지
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    • 제8권5호
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    • pp.45-50
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    • 1998
  • 본 연구에서는 입출력 데이터로부터 비선형 다변수 모델을 자동 인식할 수 있는 적응형 Spline모델링(ASMOD : Adaptive Spline Modeling of Observation Data)과 혼합 곡선 근사법(Hybrid curve approximation)을 이용한 3차원 자유 형상 설계방법을 제안하고, 초기 선형 설계 단계에서 횡단면적 곡선(SAC : Sectional Area Curve) 생성 예를 통해 그 응용 가능성을 검토하였다. 즉 실적선의 SAC를 Bspline 근사법(Fitting methdo)과 유전자 알고리즘(Genetic Algorithm)에 의해 정의하여, 조정점(Control points)에 대한 데이터베이스를 구축한다. 구축된 데이터베이스-주요치수와 이들 조정점관의 관계-를 학습 데이터로 하여 ASMOD를 학습시킨후 , SAC결정을 위한 ASMOD 모델링을 구축한다. 다른 선형 특성 곡선들-design waterline curve, bottom tangent line, center profile line-에 대해서도 동일하게 적용하여 ASMOD를 모델링할 수 있으며, 이들 선형 특성 곡선들을 결합하여 초기 선형을 생성한다.

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MR영상의 3차원 가시화 및 분석을 위한 뇌영역의 자동 분할 (Automatic Brain Segmentation for 3D Visualization and Analysis of MR Image Sets)

  • 김태우
    • 한국정보처리학회논문지
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    • 제7권2호
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    • pp.542-551
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    • 2000
  • 본 논문에서는 MR 영상의 3차원 가시화 및 분석을 위한 단일 채널 MR 영상의 자동 뇌영역 분할 방법을 제안한다. 이 방법은 4단계의 2차원 및 3차원 처리에 의하여 뇌윤곽을 찾아낸다. 1,2단계에서는 곡선 적합을 이용한 자동 문턱치화에 의하여 머리마스크와 초기 뇌마스크를 생성한다. 3단계에서 입방보간으로 초기 뇌마스크의 3차원 볼륨을 생성하여 형태학적 연산, 연결부위 레이블링에 의하여 중기 뇌마스크를 생성한다. 최종적으로 곡선 적합에 의한 자동 문턱치화를 이용하여 뇌마스크를 정련한다. 제안한 알고리즘은 영상의 슬라이스 방향을 고려할 필요가 없고 영상이 뇌 전체를 포함하지 않아도 되며, T1, T2, PD, SPGR등 다양한 종류의 MR 영상의 자동적인 뇌영역의 분할에 유용하다. 실험에서 20세트 MR 영상에 대하여 수동분할을 기준으로 0.97 이상의 유지도를 보였다.

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RFID/USN 기반 지능형 가스안전관리 서비스를 위한 자율적 분석 연구 (A Study on Autonomic Analysis for Servicing Intelligent Gas Safety Management Based on RFID/USN)

  • 오정석;최경석;권정락;윤기봉
    • 한국안전학회지
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    • 제23권6호
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    • pp.51-56
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    • 2008
  • As RFID/USN technology is used in the latest industry trend, the information analysis paradigm shifts to intelligence service environment. The intelligent service includes autonomic operation, which select activity by defining itself to the status of industry facilities. Furthermore, information analysis based on IT used to frequently data mining for detecting the meaning information and deriving new pattern. This paper suggest self-classifying of context-aware by applying data mining in gas facilities for serving the intelligent gas safety management. We modify data algorithm for fitting the domain of gas safety, construct context-aware model by using the proposed algorithm, and demonstrate our method. As the accuracy of our model is improved over 90%, the our approach can apply to intelligent gas safety management based on RFID/USN environments.

입체적인 데이터에서 애매성-프리 표면 재구성 (An Ambiguity-free Surface Construction from Volume Data)

  • 이의택;오광만;박규호
    • 한국컴퓨터그래픽스학회논문지
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    • 제4권1호
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    • pp.55-66
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    • 1998
  • This paper presents a simple method for relieving the ambiguity problems within the sub-voxel based surface-fitting approach for the surface construction. ECB algorithm is proposed to avoid the ambiguity problem which is the root of the holes within the resulting polygon based approximation. The basic idea of our disambiguation strategy is the use of a set of predefined modeling primitives (we call SMP) which guarantees the topological consistency of resulted surface polygons. 20 SMPs are derived from the extension of the concept of the elementary modeling primitives in the CB algorithm [3], and fit one to five faces of them to the iso-surface crossing a cell with no further processing. A look-up table which has a surface triangle list is pre-calculated using these 20 SMPs. All of surface triangles in the table are from the faces of SMPs and are stored in the form of edge list on which vertices of each surface triangle are located. The resulted polygon based approximation is unique at every threshold value and its validity is guaranteed without considering the complicated problems such as average of density and postprocessing. ECB algorithm could be free from the need for the time consuming post-processing, which eliminates holes by revisiting every boundary cell. Through three experiments of surface construction from volume data, its capability of hole avoidance is showed.

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Research on data augmentation algorithm for time series based on deep learning

  • Shiyu Liu;Hongyan Qiao;Lianhong Yuan;Yuan Yuan;Jun Liu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권6호
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    • pp.1530-1544
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    • 2023
  • Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

Energy Based Multiple Refitting for Skinning

  • Jha, Kailash
    • International Journal of CAD/CAM
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    • 제5권1호
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    • pp.11-18
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    • 2005
  • The traditional method of manipulation of knots and degrees gives poor quality of surface, if compatibility of input curves is not good enough. In this work, a new algorithm of multiple refitting of curves has been developed using minimum energy based formulation to get compatible curves for skinning. The present technique first reduces the number of control points and gives smoother surface for given accuracy and the surface obtained is then skinned by compatible curves. This technique is very useful to reduce data size when a large number of data have to be handled. Energy based technique is suitable for approximating the missing data. The volumetric information can also be obtained from the surface data for analysis.

Bayesian analysis for the bivariate Poisson regression model: Applications to road safety countermeasures

  • Choe, Hyeong-Gu;Lim, Joon-Beom;Won, Yong-Ho;Lee, Soo-Beom;Kim, Seong-W.
    • Journal of the Korean Data and Information Science Society
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    • 제23권4호
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    • pp.851-858
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    • 2012
  • We consider a bivariate Poisson regression model to analyze discrete count data when two dependent variables are present. We estimate the regression coefficients as sociated with several safety countermeasures. We use Markov chain and Monte Carlo techniques to execute some computations. A simulation and real data analysis are performed to demonstrate model fitting performances of the proposed model.

Design, Implementation, and Flight Tests of a Feedback Linearization Controller for Multirotor UAVs

  • Lee, Dasol;Lee, Hanseob;Lee, Jaehyun;Shim, David Hyunchul
    • International Journal of Aeronautical and Space Sciences
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    • 제18권4호
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    • pp.740-756
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    • 2017
  • This paper proposes a feedback-linearization-based control algorithm for multirotor unmanned aerial vehicles (UAVs). The feedback linearization scheme is highly efficient for considering nonlinearity between the rotational and translational motion of multirotor UAVs. We also propose a dynamic equation that reflects the aerodynamic effects of the vehicles; the equation's parameters can be determined through curve fitting using actual flight data. We derive the feedback linearization controller from the proposed dynamic equation, and propose a Luenberger observer to attenuate measurement noises. The proposed algorithm is implemented using our in-house flight control computer, and we describe its implementation in detail. To investigate the performance of the proposed algorithm, we carry out two flight scenarios: the first scenario, an autonomous landing on a moving platform, is a test of maneuverability; the second, picking up and replacing an object, test the algorithm's accuracy. In these scenarios, the proposed algorithm precisely controls multirotor UAVs, and we confirm that it can be successfully applied to real flight environments.

주파수 영역 모델 방법을 이용한 평판 구조물의 능동 소음전달 제어 (Active Noise Transmission Control Through a Panel Structure Using a Frequency Domain Identification Method)

  • 김영식;김인수;문찬영
    • 한국정밀공학회지
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    • 제18권9호
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    • pp.71-81
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    • 2001
  • This paper analyzes the effectiveness of minimizing vibration and sound transmission on/through a thin rectangular plate by both feedback control and hybrid control which combines adaptive feedforward control with a feedback loop. An experimental system identification technique using the matrix-fractional curve-fitting of the frequency response data is introduced for complex shaped structures. This identification technique reduces the model order o the MIMO(Multi-Input Multi-Output) system which simplifies the practical implementation. The adaptive feedforward control uses a Multiple filtered-x LMS(Least Mean Square) algorithm and the feedback control uses a multivariable digital LQG(Linear Quadratic Gaussian) algorithm. Experimental results show that an effective reduction of sound transmission is achieved by the hybrid control scheme when both vibration and noise measurement signals are incorporated in the controller.

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