• Title/Summary/Keyword: generalization

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GENERALIZATION OF EXTENDED BETA FUNCTION, HYPERGEOMETRIC AND CONFLUENT HYPERGEOMETRIC FUNCTIONS

  • Lee, Dong-Myung;Rathie, Arjun K.;Parmar, Rakesh K.;Kim, Yong-Sup
    • Honam Mathematical Journal
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    • v.33 no.2
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    • pp.187-206
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    • 2011
  • The main object of this paper is to present generalization of extended beta function, extended hypergeometric and confluent hypergeometric function introduced by Chaudhry et al. and obtained various integral representations, properties of beta function, Mellin transform, beta distribution, differentiation formulas transform formulas, recurrence relations, summation formula for these new generalization.

A GENERALIZATION OF THE EXPONENTIAL INTEGRAL AND SOME ASSOCIATED INEQUALITIES

  • Nantomah, Kwara;Merovci, Faton;Nasiru, Suleman
    • Honam Mathematical Journal
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    • v.39 no.1
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    • pp.49-59
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    • 2017
  • In this paper, a generalization of the exponential integral is given. As a consequence, several inequalities involving the generalized function are derived. Among other analytical techniques, the procedure utilizes the $H{\ddot{o}}lder^{\prime}s$ and Minkowskis inequalities for integrals.

Time Series Prediction Using a Multi-layer Neural Network with Low Pass Filter Characteristics (저주파 필터 특성을 갖는 다층 구조 신경망을 이용한 시계열 데이터 예측)

  • Min-Ho Lee
    • Journal of Advanced Marine Engineering and Technology
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    • v.21 no.1
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    • pp.66-70
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    • 1997
  • In this paper a new learning algorithm for curvature smoothing and improved generalization for multi-layer neural networks is proposed. To enhance the generalization ability a constraint term of hidden neuron activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. When the total cost consisted of the output error and hidden error is minimized by gradient-descent methods, the additional descent term gives not only the Hebbian learning but also the synaptic weight decay. Therefore it incorporates error back-propagation, Hebbian, and weight decay, and additional computational requirements to the standard error back-propagation is negligible. From the computer simulation of the time series prediction with Santafe competition data it is shown that the proposed learning algorithm gives much better generalization performance.

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Grassmann's Mathematical Epistemology and Generalization of Vector Spaces (그라스만의 수학 인식과 벡터공간의 일반화)

  • Lee, Hee Jung;Shin, Kyunghee
    • Journal for History of Mathematics
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    • v.26 no.4
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    • pp.245-257
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    • 2013
  • Hermann Grassmann classified mathematics and extended the dimension of vector spaces by using dialectics of contrasts. In this paper, we investigate his mathematical idea and its background, and the process of the classification of mathematics. He made a synthetic concept of mathematics based on his idea of 'equal' and 'inequal', 'discrete' and 'indiscrete' mathematics. Also, he showed a creation of new mathematics and a process of generalization using a dialectic of contrast of 'special' and 'general', 'real' and 'formal'. In addition, we examine his unique development in using 'real' and 'formal' in a process of generalization of basis and dimension of a vector space. This research on Grassmann will give meaningful suggestion to an effective teaching and learning of linear algebra.

Line Segmentation Method using Expansible Moving Window for Cartographic Linear Features (확장형 이동창을 이용한 지도 선형 개체의 분할 기법 연구)

  • Park, Woo-Jin;Lee, Jae-Eun;Yu, Ki-Yun
    • Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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    • 2010.04a
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    • pp.5-6
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    • 2010
  • Needs for the methodology of segmentation of linear feature according to the shape characteristics of line feature are increasing in cartographic linear generalization. In this study, the line segmentation method using expansible moving window is presented. This method analyzes the generalization effect of line simplification algorithms depend on the line characters of linear feature and extracts the sections which show exclusively low positional error due to a specific algorithm. The description measurements of these segments are calculated and the target line data are segmented based on the measurements. For segmenting the linear feature to a homogeneous section, expansible moving window is applied. This segmentation method is expected to be used in the cartographic map generalization considering the shape characteristics of linear feature.

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More Comments on Non-Normal Process Capability Indices : $C_{Np}$(U, V, W) (비정규 공정의 공정능력지수에 관한 고찰 : $C_{Np}$(U, V, W))

  • 김진수;김홍준
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.25 no.6
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    • pp.17-22
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    • 2002
  • In this paper, We consider some generalization of these five basic indices to cover non-normal distribution. The proposed generalizations are compared with the five basic indices. The results show that the proposed generalizations are more accurate than those basic indices and other generalization in measuring process capability. We compared an estimation methods by Clements with based on sample percentiles WVM to calculate the proposed generalization as an example The results indicated that Clements method is more accurate than percentile method, WVM in measuring process capability But the calculations of percentile method are easy to understand, straightforward to apply, and show be valuable used for applications.

Generalization of Road Network using Logistic Regression

  • Park, Woojin;Huh, Yong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.37 no.2
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    • pp.91-97
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    • 2019
  • In automatic map generalization, the formalization of cartographic principles is important. This study proposes and evaluates the selection method for road network generalization that analyzes existing maps using reverse engineering and formalizes the selection rules for the road network. Existing maps with a 1:5,000 scale and a 1:25,000 scale are compared, and the criteria for selection of the road network data and the relative importance of each network object are determined and analyzed using $T{\ddot{o}}pfer^{\prime}s$ Radical Law as well as the logistic regression model. The selection model derived from the analysis result is applied to the test data, and road network data for the 1:25,000 scale map are generated from the digital topographic map on a 1:5,000 scale. The selected road network is compared with the existing road network data on the 1:25,000 scale for a qualitative and quantitative evaluation. The result indicates that more than 80% of road objects are matched to existing data.

A Study on the Cartographic Generalization of Stream Networks by Rule-based Modelling (규칙기반 모델링에 의한 하계망 일반화에 관한 연구)

  • Kim Nam-Shin
    • Journal of the Korean Geographical Society
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    • v.39 no.4
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    • pp.633-642
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    • 2004
  • This study tries to generalize the stream network by constructing rule-based modelling. A study on the map generalization tends to be concentrated on development of algorithms for modification of linear features and evaluations to the limited cartographic elements. Rule-based modelling can help to improve previous algorithms by application of generalization process with the results that analyzing mapping principles and spatial distribution patterns of geographical phenomena. Rule-based modelling can be applied to generalize various cartographic elements, and make an effective on multi-scaling mapping in the digital environments. In this research, nile-based modelling for stream network is composed of generalization rule, algorithm for centerline extraction and linear features. Before generalization, drainage pattern was analyzed by the connectivity with lake to minimize logical errors. As a result, 17 streams with centerline are extracted from 108 double-lined streams. Total length of stream networks is reduced as 17% in 1:25,000 scale, and as 29% in 1:50,000. Simoo algorithm, which is developed to generalize linear features, is compared to Douglas-Peucker(D-P) algorithm. D-P made linear features rough due to the increase of data point distance and widening of external angle. But in Simoo, linear features are smoothed with the decrease of scale.

Generalization by LoD and Coordinate Transformation in On-the-demand Web Mapping (웹환경에서 LoD와 좌표변형에 의한 지도일반화)

  • Kim, Nam-Shin
    • Journal of the Korean association of regional geographers
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    • v.15 no.2
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    • pp.307-315
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    • 2009
  • The purpose of map generalization is a method of map making to transmit the concise cartographic representation and geographic meaning. New generalization algorithm has been developed to be applied in the digital environments by the development of computer cartography. This study aims to look into possibilities of the multiscale mapping by generalization in application with the coordinate transformation and LoD(level of detail) in the web cartography. A method of the coordinate transformation is to improve a transmission of spatial data. Lod is a method which is making web map with selection spatial data by zoom level of users. Layers for test constructed contour line, stream network, the name of a place, a summit of mountain, and administrative office. The generalization was applied to zoom levels by scale for the linear and polygonal features using XML-Based scalable vector graphics(SVG). Resultantly, storage capacity of data was minimized 41% from 9.76mb to 4.08mb in SVG. Generalization of LoD was applied to map elements by stages of the zoom level. In the first stages of zoom level, the main name of places and administrative office, higher order of stream channels, main summit of mountain was represented, and become increase numbers of map elements in the higher levels. Results of this study can help to improve esthetic map and data minimization in web cartography, and also need to make an efforts to research an algorithm on the map generalization over the web.

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