• ETRI Journal
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• 제42권1호
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• pp.90-100
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• 2020

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state-of-the-art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short-term memory and back propagation through time approaches. The proposed models consider both language-dependent features, such as part-of-speech tags, and language-independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f-measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.

• 인터넷정보학회논문지
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• 제17권5호
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• pp.111-119
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• 2016

In recent years, the opinion analysis is one of the key research fronts of any domain. Opinion target extraction is an essential process of opinion analysis. Target is usually referred to noun or noun phrase in an entity which is deliberated by the opinion holder. Extraction of opinion target facilitates the opinion analysis more precisely and in addition helps to identify the opinion polarity i.e. users can perceive opinion in detail of a target including all its features. One of the most commonly employed algorithms is a sequence labeling algorithm also called Conditional Random Fields. In present article, recent opinion target extraction approaches are reviewed based on sequence labeling algorithm and it features combinations by analyzing and comparing these approaches. The good selection of features combinations will in some way give a good or better accuracy result. Features combinations are an essential process that can be used to identify and remove unneeded, irrelevant and redundant attributes from data that do not contribute to the accuracy of a predictive model or may in fact decrease the accuracy of the model. Hence, in general this review eventually leads to the contribution for the opinion analysis approach and assist researcher for the opinion target extraction in particular.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4405-4418
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• 2016

Automatic image completion techniques have difficulty processing images in which the target region has multiple planes or is non-facade. Here, we propose a new image completion method that uses belief propagation based on planar priorities. We first calculate planar information, which includes planar projection parameters, plane segments, and repetitive regularity extractions within the plane. Next, we convert this planar information into planar guide knowledge using the prior probabilities of patch transforms and offsets. Using the energy of the discrete Markov Random Field (MRF), we then define an objective function for image completion that uses the planar guide knowledge. Finally, in order to effectively optimize the MRF, we propose a new optimization scheme, termed Planar Priority-belief propagation that includes message-scheduling-based planar priority and dynamic label cropping. The results of experiment show that our approach exhibits advanced performance compared with existing approaches.

• Communications for Statistical Applications and Methods
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• 제25권2호
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• pp.217-234
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• 2018

Copulas are a tool for constructing multivariate distributions and formalizing the dependence structure between random variables. From copula literature review, there are a few asymmetric copulas available so far while data collected from the real world often exhibit asymmetric nature. This necessitates developing asymmetric copulas. In this study, we discuss a method to construct a new class of bivariate asymmetric copulas based on products of symmetric (sometimes asymmetric) copulas with powered arguments in order to determine if the proposed construction can offer an added value for modeling asymmetric bivariate data. With these newly constructed copulas, we investigate dependence properties and measure of association between random variables. In addition, the test of symmetry of data and the estimation of hyper-parameters by the maximum likelihood method are discussed. With two real example such as car rental data and economic indicators data, we perform the goodness-of-fit test of our proposed asymmetric copulas. For these data, some of the proposed models turned out to be successful whereas the existing copulas were mostly unsuccessful. The method of presented here can be useful in fields such as finance, climate and social science.

• 대한전자공학회논문지SP
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• 제42권3호
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• pp.1-6
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• 2005

변이 영상을 마코브 랜덤필드(MRF)로 모델링한 마코브 네트워크에서 신뢰확산 알고리듬은 각 화소에 대응되는 노드들 사이에 메시지를 전달하는 방식으로 이루어진다. 최초 메시지는 알고리듬의 반복을 통해 특정한 값으로 수렴하게 되며, 수렴된 값을 얻기 위해서는 많은 알고리듬의 반복이 필요하다. 본 논문에서는 알고리듬의 반복을 줄이기 위해 영상내 물체들을 선들의 조합 구성으로 보고 각각의 선들은 같은 메시지를 갖는 노드들의 집합으로 간주하여 기존의 신뢰확산 알고리듬을 단순화하였다.

• 한국정밀공학회지
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• 제19권12호
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• pp.70-77
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• 2002

Necessity of the tole-operation has been increased in many fields. Since the Internet is inexpensive and available all over the world, it is a strong candidate for the transmission media of the tole-operation. However, the Internet has random time delays that may cause instability in the system especially if the tole -operation is bilateral. In the past few years many attempts have been made to overcome the random time delay, So far, they are still insufficient in terms of performance. The ‘Variable holding time’ is introduced to improve the performance of the ‘Event based tole-operation’ which controls a system with a non-time action reference. By holding each event for proper time, the system can quickly respond and be stabilized. The proper holding time should be selected based on the characteristics of the task that the system performs. The factors that reflect those characteristics are investigated. The fuzzy logic is employed to obtain the proper holding time for each event while the tole-operation system is in operation. The experimental results presented in this paper verify effectiveness of the proposed method.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1992년도 추계학술대회논문집; 반도아카데미, 20 Nov. 1992
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• pp.117-123
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• 1992

In recent years, the finite element method has become one of the most popular numerical technique for obtaining solutions of engineering science problems. However, there exist various uncertainties in modeling the problems, such as the dimensions(geometry shape), the material properties, boundary conditions, etc. The consideration for the uncertainties inherent in the problems can be made by understanding the influences of uncertain parameters[1]. Determining the influences of uncertainties as statistical quantities using the standard finite element method requires enormous computing time, while the probabilistic finite element method is realized as an efficient scheme[2,3] yielding statistical solution with just a few direct computations. In this paper, a formulation of the probabilistic fluid-structure interaction problem accounting for the first order perturbation of geometric shape is derived, and especially probabilistical acoustic pressure scattering from the structure with surrounding fluid is focused on. In Section 2, governing equations for the fluid-structure problems are given. In Section 3, a finite element formulation, based on the functional, is presented. First order perturbation of geometric shape with randomness is incorporated into the finite element formulation in conjunction with discretization of the random fields in Section 4 and 5. Finally, the proposed formulation is applied to a acoustic pressure scattering problem from an infinitely long cylindrical shell structure with randomness of radial perturbation.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2007년도 정기학술대회
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• pp.303-310
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• 2007

Named Entity Recognition (NER) is always limited by its lower recall resulting from the asymmetric data distribution where the NONE class dominates the entity classes. This paper presents an approach that exploits non-local information to improve the NER recall. Several kinds of non-local features encoding entity token occurrence, entity boundary and entity class are explored under Conditional Random Fields (CRFs) framework. Experiments on SIGHAN 2006 MSRA (CityU) corpus indicate that non-local features can effectively enhance the recall of the state-of-the-art NER systems. Incorporating the non-local features into the NER systems using local features alone, our best system achieves a 23.56% (25.26%) relative error reduction on the recall and 17.10% (11.36%) relative error reduction on the F1 score; the improved F1 score 89.38% (90.09%) is significantly superior to the best NER system with F1 of 86.51% (89.03%) participated in the closed track.

• Water Engineering Research
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• 제3권1호
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• pp.31-44
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• 2002

The effect of diurnal cycle, intermittent visit of observation satellite, sensor installation, partial coverage of remote sensing, heterogeneity of soil properties and precipitation to the soil moisture estimation error were analyzed to present the global sampling strategy of soil moisture. Three models, the theoretical soil moisture model, WGR model proposed Waymire of at. (1984) to generate rainfall, and Turning Band Method to generate two dimensional soil porosity, active soil depth and loss coefficient field were used to construct sufficient two-dimensional soil moisture data based on different scenarios. The sampling error is dominated by sampling interval and design scheme. The effect of heterogeneity of soil properties and rainfall to sampling error is smaller than that of temporal gap and spatial gap. Selecting a small sampling interval can dramatically reduce the sampling error generated by other factors such as heterogeneity of rainfall, soil properties, topography, and climatic conditions. If the annual mean of coverage portion is about 90%, the effect of partial coverage to sampling error can be disregarded. The water retention capacity of fields is very important in the sampling error. The smaller the water retention capacity of the field (small soil porosity and thin active soil depth), the greater the sampling error. These results indicate that the sampling error is very sensitive to water retention capacity. Block random installation gets more accurate data than random installation of soil moisture gages. The Walnut Gulch soil moisture data show that the diurnal variation of soil moisture causes sampling error between 1 and 4 % in daily estimation.

• 한국산학기술학회논문지
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• 제11권5호
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• pp.1653-1660
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• 2010

데이터베이스에 저장된 고객 정보들에 대한 유출 사례가 빈번히 발생하고 있다. 악의적인 목적을 갖고 있는 내부 관리자나 외부 공격자로부터 정보를 막기 위해서는 정보를 암호화하여 DB에 저장하는 것이 가장 효율적인 방법 중 하나이다. 암호화를 해 놓고선 DB에 저장만 하여놓고 다른 어떤 활용도 할 수 없다면 차라리 파기하는 편이 나을 것이다. 암호화된 DB 검색시스템이 다양하게 발전하고 있고 여러 분야에서 활용되고 있다. 본 논문에서는 모바일 디바이스에서 신뢰할 수 없는 서버에게 사용자의 정보를 노출하지 않고 암호화된 문서를 검색할 수 있는 스킴을 구현하고 비교분석을 하였다. 구현 결과를 대칭키 기반의 DES, AES, ARIA별로 검색시간을 비교 분석하였다.