Earticle

In this paper, we propose a method to derive the kernel function directly from the measured apparent earth resistivity. At this time, the kernel function is obtained through the process of solving a nonlinear system. Nonlinear systems with many variables are difficult to solve. This paper also introduces a method for converting nonlinear derived systems to linear systems. The kernel function is a function of the depth and resistance of the Earth's layer. Being able to derive an accurate kernel function means that we can estimate the earth parameters i.e. layer depth and resistivity. We also use various Earth models as simulation examples to validate the proposed method.

In this article, we research and design a new vehicle license plate recognition system in traffic management system, this new system will solve a lot of problems about vehicle. The system include two parts, "Plate Detect" and "Chars Recognize", we did pretreatment in the first part, and using our improved fusion kernel function SVM to detect the plate, and in the second part, we segmented every single char batch, and used the new deep learning model CNN model to implement recognizing alphabets and numbers, this system recognition accuracy rate will over 97%, can be a part of traffic management system in Smart City.

This paper proposes an improved Markov prediction, according to the temperature control problem of hot blast stove alternate supply air in the operation of blast furnace operation, namely, implement clustering for waiting to be processed data, used kernel fuzzy c-means clustering based on the pairwise constraints. The supply air temperature of hot blast stove is seen as without aftereffect things in this method, introduce semi-supervised learning mechanism in traditional fuzzy clustering to deal with the basic data, and using the kernel effectiveness index improved the FCM algorithm. Experiments show that the improved clustering algorithm is superior to other algorithms in accuracy and performance, at the same time, the improved prediction model comparison with the traditional values of temperature prediction, which has obvious advantages in defined temperature range and the fit of the temperature value, the guiding significance was significantly enhanced in industrial field.

The parameters of support vector machine have a great influence on the learning ability and generalization ability, so an improved ant colony optimization algorithm is proposed to optimize the parameters of SVM, then an optimized SVM classifier (IMACO-SVM) is proposed for data classification. In the IMACO-SVM, the adaptive adjustment pheromone strategy is used to make relatively uniform pheromone distribution and the improved pheromone updating method is used to submerge the heuristic factor by the residual pheromone information, in order to effectively solve the contradiction between expanding search and finding optimal solution. The selection of parameters of the SVM is regarded as a combination optimization of parameters in order to establish the objective function of combination optimization. The improved ACO algorithm with good robustness and positive feedback characteristics and parallel searching is used to search for the optimal value of objective function. In order to validate the classification effectiveness of the IMACO-SVM algorithm, some experimental data from the UCI machine learning database are selected in this paper. The classification results show that the proposed IMACO-SVM algorithm has higher classification ability and classification accuracy.

Because of the nonlinear characteristics of monitoring system in military barracks, the traditional KPCA method either have low sensitivity or unable to detect the fault quickly and accurately. In order to make use of higher-order statistics to get more useful information and meet the requirements of real-time fault diagnosis and sensitivity, a new method of fault detection and diagnosis is proposed based on multivariate statistical kernel principal component analysis (MSKPCA), which combines statistic pattern analysis framework (SPA) and kernel principal component analysis (KPCA). First, the transformation of multivariate statistics and kernel function are conducted in which technology of moving time window is used. Then, PCA is executed to analysis the kernel function obtained from the first step. Moreover, the statistics of T^2 and SPE and the control limits of them are calculated. Finally, simulations on a typical nonlinear numerical example show that the proposed MSKPCA method is more effective than PCA and KPCA in terms of fault detection and diagnosis.

In this article, aiming at complex prediction problems of the rail transit passenger flow and prediction problems combined with the actual situation of rail transit in Beijing. We will propose a fusion model of passenger flow for predicting. It can improve the prediction accuracy for passenger flow forecasting. We use the partial least squares regression method to solve multicollinearity between the dependent variable. The method of principal component analysis can rescreen the all the factors which are affect the passenger flow. To extract comprehensive variable this has the best ability to explain the passenger flow from all of the information, in order to solve the relevance of statistics, noise and information redundancy. The nonlinear prediction model which is between comprehensive variable and passenger flow will be established. Finally, through the passenger flow forecasting of exchange station of Beijing Metro Line 1 to verify the effectiveness of the method.

The estimation of soil parameters of multilayer structure leads to useful information for designing a safe grounding system. This paper presents a nonlinear regression based estimation scheme to extract soil parameters from the kernel function of apparent earth resistivity. The kernel function of apparent earth resistivity can be obtained from the measured apparent earth resistivity data. The performance of the proposed method has been verified by carrying out a numerical example.

The variation of facial appearance due to the viewpoint or pose obviously degrades the accuracy of any face recognition systems. One solution is generating the virtual frontal view from any given non-frontal view to obtain a virtual gallery/probe face. As the state-of-the-art face recognition algorithm, linear regression computes a reconstruction matrix from the images of each subject and then approximates the probe face image by using the reconstruction matrix, but the performance of this linear algorithm is limited due to the nonlinear structure of the face images which is caused by variations in illumination, expression, pose and occlusion. Following this idea, in this paper, we propose an efficient and novel locally kernel-based nonlinear regression (LKNR) method, which generates the virtual frontal view from a given non-frontal face image. Because of the high (even infinite) dimensionality of the nonlinear transformation functions, it is infeasible to directly calculate the corresponding reconstruction matrix and therefore is unable to approximate explicitly the probe image. So, with the help of kernel functions, we overcome to this mentioned problem by embedding the nonlinear regression in the stage of computing the reconstruction matrix from the non-frontal input face and non-frontal face database. The comparison of the proposed method with locally linear regression (LLR) and eigen light-field (ELF) methods is also provided in terms of the face recognition accuracy. Experimental results show that the proposed method outperforms two other methods in terms of robustness and visual effects.

In this paper we propose a model of classification based on the principle of the fuzzy proximity of the terms within the documents. Given the heterogeneous nature of the Arabic documents in our possession, we have studied for this purpose the research model based on the semantic proximity of terms and inspired from the classic Boolean model. Our approach is based on the assumption that more the occurrences of terms in query are close with good connectivity in the extracted semantic graph from the set of document , more this document is relevant to this query. We propose a measure that provides a contextual and semantic search. We used not only a semantic graph to highlight the semantic connections between terms, but also an auxiliary dictionary to increase the connectivity of the graph and therefore the discrimination of documents relevant to the query.

Traditionally, the key idea of estimating independent component analysis (ICA) model is to maximize the non-Gaussianity, however, often with the assumption that density of data is near the standardized Gaussian density. To avoid the unsuitable assumption, this article uses the nonparametric density estimating method. A nonparametric independent component analysis algorithm based on Epanechnikov kernel function is proposed in this paper. This algorithm uses the Epanechnikov kernel estimator to estimate random variable distribution, meanwhile, employs the hypothesis test to derive the nonparametric likelihood ratio (NLR) objective function. For optimizing the nonparametric density estimation, the selection of kernel function and bandwidth is crucial. From the perspective of minimizing the mean integrated square error (MISE), this paper discusses the optimal selection and conducts experiments for further study. To increase the algorithmic convergence rate and reduce the running time, the quasi-newton method has been used to optimize the objective function. Compared with previous nonparametric ICA algorithm, the simulation results demonstrate that the proposed method offers better performance both on speech separation and computing capability.

수공구조물의 설계에 있어 가중 중요한 변수 중에 하나가 확률 강우량이다. 우리나라의 경우 매개변수적인 지점빈도해석을 통해 확률 강우량을 산정하고 있으나, 최근 들어 지점별 관측자료의 부족으로 인한 지역빈도해석을 수행하여 확률강우량을 산정하고 있는 실정이다. Index Flood 기법이나 L-moment 기법과 같은 기존의 지역빈도해석은 여러 관측 지점에서 관측된 강우자료를 이용하여 매년최대 시간강우량 자료를 추출하여 동질성 분석을 통해 이질성이 없는 것으로 분석된 연최대 강우량을 빈도해석 하여 확률 강우량을 결정한다. 그러나 이와 같은 지역빈도해석은 매개변수적 지점빈도해석과 마찬가지로 적합도 검정에 통과한 다수의 분포형이 선정되는 경우에 어떤 분포형을 사용하느냐 하는 문제점이 발생할 수 있다. 그리고 선정된 여러 강우 관측 지점의 연최대 강우량 자료에 모두 동일한 확률 분포형을 이용하므로 선정된 확률 분포형이 모든 지점의 강우 자료와 적합하지 못할 가능성을 내포하고 있으며, 또한 수문자료가 여러가지 요인으로 인하여 복합분포(mixed distribution)형태를 가질 때, 매개변수적 해석방법으로는 다중 첨두를 갖는 확률밀도함수를 해석하는데는 여러 가지 어려움이 따른다. 따라서 이러한 매개변수적 확률분포형을 이용한 빈도해석의 문제점을 해결할 수 있는 비매개변수적 빈도해석이 하나의 대안으로 제시될 수 있다. 본 연구에서는 강우자료의 선별을 통해 신뢰성 있는 자료를 구축하고, 기존의 매개변수를 갖는 확률 분포형을 이용한 지역빈도해석을 적용하여 확률 강우량을 산정하였다. 그리고 동질성분석을 통해 선정된 강우자료에 대해 비매개변수적 지역빈도해석을 적용하여 확률 강우량을 산정하고 각각의 방법에 대한 빈도해석 결과를 비교하여 확률강우량 해석에 있어 하나의 대안을 제시하고자 한다.X>${\mu}_{max,A}$는 최대암모니아 섭취률을 이용하여 구한 결과 $0.65d^{-1}$로 나타났다.EX>$</TEX>60%{\sim}87%$</TEX>가 수심 10m 이내에 분포하였고, 녹조강과 남조강이 우점하는 하절기에는 5m 이내에 주로 분포하였다. 취수탑 지점의 수심이 연중 $25{\sim}35m$를 유지하는 H호의 경우 간헐식 폭기장치를 가동하는 기간은 물론 그 외 기간에도 취수구의 심도를 표층 10m 이하로 유지 할 경우 전체 조류 유입량을 60% 이상 저감할 수 있을 것으로 조사되었다.심볼 및 색채 디자인 등의 작업이 수반되어야 하며, 이들을 고려한 인터넷용 GIS기본도를 신규 제작한다. 상습침수지구와 관련된 각종 GIS데이타와 각 기관이 보유하고 있는 공공정보 가운데 공간정보와 연계되어야 하는 자료를 인터넷 GIS를 이용하여 효율적으로 관리하기 위해서는 단계별 구축전략이 필요하다. 따라서 본 논문에서는 인터넷 GIS를 이용하여 상습침수구역관련 정보를 검색, 처리 및 분석할 수 있는 상습침수 구역 종합정보화 시스템을 구축토록 하였다.N, 항목에서 보 상류가 높게 나타났으나, 철거되지 않은 검전보나 안양대교보에 비해 그 차이가 크지 않은 것으로 나타났다.의 기상변화가 자발성 기흉 발생에 영향을 미친다고 추론할 수 있었다. 향후 본 연구에서 추론된 기상변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을

We compute the holomorphic derivative of the harmonic measure associated to a $C^\infty$bounded domain in the plane and show that the exact Bergman kernel function associated to a $C^\infty$ bounded domain in the plane relates the derivatives of the Ahlfors map and the Szego kernel in an explicit way. We find several formulas for the exact Bergman kernel and the Szego kernel and the harmonic measure. Finally we survey some other properties of the holomorphic derivative of the harmonic measure.

In this paper, we express the Green function in terms of the classical kernel functions in potential theory. In particular, we obtain a formula relating the Green function and the Szeg? kernel function which consists of only the Szeg? kernel function in a $C^{\infty}$ smoothly bounded finitely connected domain in the complex plane.

The purpose of this paper is to propose the kernel density estimator and the jackknife kernel density estimator in the presence of k's unidentified outliers, and to compare the small sample performances of the proposed estimators in a sense of mean integrated square error(MISE).

Because a wide variety of multimedia services are provided through personal wireless communication devices, the demand for efficient bandwidth utilization becomes stronger. This demand naturally results in the introduction of the variable bitrate speech coding concept. One exemplary work is the selectable mode vocoder (SMV) that supports speech/music classification. However, because it has severe limitations in its classification performance, a couple of works to improve speech/music classification by introducing support vector machines (SVMs) have been proposed. While these approaches significantly improved classification accuracy, they did not consider correlations commonly found in speech and music frames. In this paper, we propose a novel and orthogonal approach to improve the speech/music classification of SMV codec by adaptively tuning SVMs based on interframe correlations. According to the experimental results, the proposed algorithm yields improved results in classifying speech and music within the SMV framework.

Given a sequence ｛ $X_{n}$｝ of independent and identically distributed random variables with F, a sequential procedure for the p-th quantile ξ$_{P}$= $F^{-1}$ (P), 0<p<1, is proposed in which two constraints are the conditions of coverage probability and $\beta$-protection. Some asymptotic properties for the proposed procedure and of an involved stopping time are proved: asymptotic consistency, asymptotic efficiency and asymptotic normality. From one of the results an effect of smoothing based on kernel functions is discussed. The results are also extended to the contaminated case.e.e.

We study the asymptotic boundary behavior of the Bergman kernel function on the diagonal, the Bergman metric and the holomorphic sectional curvatures of the Bergman metric in bounded strongly pseudoconvex domains.

The Bergman kernel is closely connected to mapping problems in complex analysis. For example, the Riemann mapping function is witten down in terms of the Bergman kernel. Hence, information about the bergman kernel gives information about mappings. In this note, we prove the following theorem.

In this letter, a new online anomaly detection approach for software systems is proposed. The novelty of the proposed approach is to apply a new semantic kernel function for a support vector machine (SVM) classifier to detect fault-suspicious execution paths at runtime in a reasonable amount of time. The kernel uses a new sequence matching algorithm to measure similarities among program execution paths in a customized feature space whose dimensions represent the largest common subpaths among the execution paths. To increase the precision of the SVM classifier, each common subpath is given weights according to its ability to discern executions as correct or anomalous. Experiment results show that compared with the known kernels, the proposed SVM kernel will improve the time overhead of online anomaly detection by up to 170%, while improving the precision of anomaly alerts by up to 140%.