Articles de revues : « Attribute estimation »

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Kawai, Hiroya, Koichi Ito et Takafumi Aoki. « Face Attribute Estimation Using Multi-Task Convolutional Neural Network ». Journal of Imaging 8, n^o 4 (10 avril 2022) : 105. http://dx.doi.org/10.3390/jimaging8040105.

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Face attribute estimation can be used for improving the accuracy of face recognition, customer analysis in marketing, image retrieval, video surveillance, and criminal investigation. The major methods for face attribute estimation are based on Convolutional Neural Networks (CNNs) that solve face attribute estimation as a multiple two-class classification problem. Although one feature extractor should be used for each attribute to explore the accuracy of attribute estimation, in most cases, one feature extractor is shared to estimate all face attributes for the parameter efficiency. This paper proposes a face attribute estimation method using Merged Multi-CNN (MM-CNN) to automatically optimize CNN structures for solving multiple binary classification problems to improve parameter efficiency and accuracy in face attribute estimation. We also propose a parameter reduction method called Convolutionalization for Parameter Reduction (CPR), which removes all fully connected layers from MM-CNNs. Through a set of experiments using the CelebA and LFW-a datasets, we demonstrate that MM-CNN with CPR exhibits higher efficiency of face attribute estimation in terms of estimation accuracy and the number of weight parameters than conventional methods.

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Duan, Shunran, Longlong Jiao, Meijuan Yin et Lanlan Yü. « DADDE:deep anomaly detection with density estimation ». Journal of Physics : Conference Series 2294, n^o 1 (1 juin 2022) : 012017. http://dx.doi.org/10.1088/1742-6596/2294/1/012017.

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Abstract The problem of abnormal nodes detection in attributed network is widely used in daily life, such as social networks, cyberspace security, financial fields and so on. Most existing detection methods ignore the relationship between structure information and attribute information in attributed network. Although some methods consider the relationship between them, but it can’t distinguish the types of abnormal nodes well, that is, attribute exceptions or structural exceptions. Aiming at the shortcomings of existing methods, this paper proposes a deep anomaly detection model combining density estimation. The idea of detecting abnormal nodes by reconstruction error is used, the structure information and attribute information of attributed network are reconstructed respectively, and abnormal nodes are detected by density estimation based on reconstruction error and embedding vector of nodes. In this paper, the validity of DADDE model is proved by experiments on three commonly used data sets in this field.

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Subroto, Agus. « SIKAP KONSUMEN TERHADAP ATRIBUT-ATRIBUT YANG DITAWARKAN TOKO BUKU GRAMEDIA DI SURABAYA ». Manajemen & ; Bisnis Jurnal 4, n^o 2 (5 septembre 2018) : 1–19. http://dx.doi.org/10.37303/embeji.v4i2.101.

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This research aim to identitication and to know consumer attitude towards attributes that offered by Gramedia book store in Surabaya, that attributes are : product, price, promotion, location, distribution and facility as independent variable and consumer attitude as dependent variable. To analysis that costumer attitude use Multiatribute Attitude Model (MAM) that substance of consumer satisfaction determined by weight which given consumer to attribute (i) times by difference consumer ideal to attribute (i) by consumer beliefing to attribute (i) accounting of measuring scale use likert scale by five estimation category which each estimation category qualificated by give weight. The results of the study prove that consumers have a positive attitude towards the attributes offered and the most influential attribute is location. Keywords: consumers attitude, attributes.

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Subroto, Agus. « SIKAP KONSUMEN TERHADAP ATRIBUT-ATRIBUT YANG DITAWARKAN TOKO BUKU GRAMEDIA DI SURABAYA ». Manajemen & ; Bisnis Jurnal 4, n^o 2 (5 septembre 2019) : 18–36. http://dx.doi.org/10.37303/embeji.v4i2.74.

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This research aim to identitication and to know consumer attitude towards attributes that offered by Gramedia book store in Surabaya, that attributes are : product, price, promotion, location, distribution and facility as independent variable and consumer attitude as dependent variable. To analysis that costumer attitude use Multiatribute Attitude Model (MAM) that substance of consumer satisfaction determined by weight which given consumer to attribute (i) times by difference consumer ideal to attribute (i) by consumer beliefing to attribute (i) accounting of measuring scale use likert scale by five estimation category which each estimation category qualificated by give weight. The results of the study prove that consumers have a positive attitude towards the attributes offered and the most influential attribute is location. Keywords: consumers attitude, attributes.

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Al-Ali, Mustafa, Richard Hastings-James, Mohammad Makkawi et Gabor Korvin. « Vibrator attribute leading velocity estimation ». Leading Edge 22, n^o 5 (mai 2003) : 400–405. http://dx.doi.org/10.1190/1.1579571.

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Yu, Xiao Feng, Zhao Sheng Luo, Chun Lei Gao et Yu Jun Li. « Influence of Attributes Relationship to Parameter Estimation in DINA ». Advanced Materials Research 940 (juin 2014) : 491–94. http://dx.doi.org/10.4028/www.scientific.net/amr.940.491.

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DINA model appreciated by many researchers considers the relationships among attributes as mutually independent, conjunction and non-compensation relationship, but in real applications, there may not be able to meet such a relationship. Study the impacts on parameter estimates for the DINA model because of attribute relationships. Simulation results show that when there is a hierarchical relationship between attributes, there will be a great impact on the parameter estimation accuracy of DINA model; and the parameter estimation accuracy mainly affected by attribute hierarchy and the number of subjects. When existing hierarchy relationships among attributes, using DINA model as cognitive diagnosis model would affect the validity of diagnostic tests.

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LI, JINGZHOU, et GUENTHER RUHE. « SOFTWARE EFFORT ESTIMATION BY ANALOGY USING ATTRIBUTE SELECTION BASED ON ROUGH SET ANALYSIS ». International Journal of Software Engineering and Knowledge Engineering 18, n^o 01 (février 2008) : 1–23. http://dx.doi.org/10.1142/s0218194008003532.

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Estimation by analogy (EBA) predicts effort for a new project by learning from the performance of former projects. This is done by aggregating effort information of similar projects from a given historical data set that contains projects, or objects in general, and attributes describing the objects. While this has been successful in general, existing research results have shown that a carefully selected subset, as well as weighting, of the attributes may improve the performance of the estimation methods. In order to improve the estimation accuracy of our former proposed EBA method AQUA, which supports data sets that have non-quantitative and missing values, an attribute weighting method using rough set analysis is proposed in this paper. AQUA is thus extended to AQUA+ by incorporating the proposed attribute weighting and selection method. Better prediction accuracy was obtained by AQUA+ compared to AQUA for five data sets. The proposed method for attribute weighting and selection is effective in that (1) it supports data sets that have non-quantitative and missing values; (2) it supports attribute selection as well as weighting, which are not supported simultaneously by other attribute selection methods; and (3) it helps AQUA+ to produce better performance.

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Tu, Dongbo, Shiyu Wang, Yan Cai, Jeff Douglas et Hua-Hua Chang. « Cognitive Diagnostic Models With Attribute Hierarchies : Model Estimation With a Restricted Q-Matrix Design ». Applied Psychological Measurement 43, n^o 4 (16 avril 2018) : 255–71. http://dx.doi.org/10.1177/0146621618765721.

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Attribute hierarchy is a common assumption in the educational context, where the mastery of one attribute is assumed to be a prerequisite to the mastery of another one. The attribute hierarchy can be incorporated through a restricted Q matrix that implies the specified structure. The latent class–based cognitive diagnostic models (CDMs) usually do not assume a hierarchical structure among attributes, which means all profiles of attributes are possible in a population of interest. This study investigates different estimation methods to the classification accuracy for a family of CDMs when they are combined with a restricted Q-matrix design. A simulation study is used to explain the misclassification caused by an unrestricted estimation procedure. The advantages of the restricted estimation procedure utilizing attribute hierarchies for increased classification accuracy are also further illustrated through a real data analysis on a syllogistic reasoning diagnostic assessment. This research can provide guidelines for educational and psychological researchers and practitioners when they use CDMs to analyze the data with a restricted Q-matrix design and make them be aware of the potentially contaminated classification results if ignoring attribute hierarchies.

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Chen, Yingbing, John A. Kershaw, Yung-Han Hsu et Ting-Ru Yang. « Carbon estimation using sampling to correct LiDAR-assisted enhanced forest inventory estimates ». Forestry Chronicle 96, n^o 01 (mai 2020) : 9–19. http://dx.doi.org/10.5558/tfc2020-003.

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Light Detection and Ranging (LiDAR) scanning has been increasingly applied in forest ecosystem surveys. Data from LiDAR describe forest structure and provide attribute information for forest inventory. These attributes can potentially aid in the estimation of biomass and carbon by providing sampling covariates. Therefore, this study explored the accuracy of estimating carbon storage by calibrating LiDAR attributes using list sampling with a ratio estimator. Standing tree carbon and down woody debris carbon were estimated across 10 broad forest types. LiDAR-derived gross total volume was used as a listing factor and big BAF samples to collect field data. Gross total volumes were “corrected” using a ratio estimator. The results show that standing tree carbon was 58.5 Mg C × ha-1 (± 2.9% SE), and dead woody debris carbon 1.8 Mg C × ha-1 (± 7.2% SE). With the exception of one forest type, these estimates were comparable to those derived from the carbon budget model of the Canadian forest sector (CBM-CFS3).

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Ortal, Patricia, et Masato Edahiro. « Similarity Measure for Product Attribute Estimation ». IEEE Access 8 (2020) : 179073–82. http://dx.doi.org/10.1109/access.2020.3027023.

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Chen, Yiliang, Shengfeng He, Zichang Tan, Chu Han, Guoqiang Han et Jing Qin. « Age estimation via attribute-region association ». Neurocomputing 367 (novembre 2019) : 346–56. http://dx.doi.org/10.1016/j.neucom.2019.08.034.

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Peng, Weishi, Yangwang Fang, XiaoJu Yong et Fang Wang. « Measures for multiple-attribute estimation ranking ». Optik 127, n^o 20 (octobre 2016) : 9479–87. http://dx.doi.org/10.1016/j.ijleo.2016.07.033.

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Majewski, Krzysztof, et Nicholas Pippenger. « Attribute estimation and testing quasi-symmetry ». Information Processing Letters 109, n^o 4 (janvier 2009) : 233–37. http://dx.doi.org/10.1016/j.ipl.2008.10.011.

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Biljecki, F., et M. Sindram. « ESTIMATING BUILDING AGE WITH 3D GIS ». ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W5 (23 octobre 2017) : 17–24. http://dx.doi.org/10.5194/isprs-annals-iv-4-w5-17-2017.

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Building datasets (e.g. footprints in OpenStreetMap and 3D city models) are becoming increasingly available worldwide. However, the thematic (attribute) aspect is not always given attention, as many of such datasets are lacking in completeness of attributes. A prominent attribute of buildings is the year of construction, which is useful for some applications, but its availability may be scarce. This paper explores the potential of estimating the year of construction (or age) of buildings from other attributes using random forest regression. The developed method has a two-fold benefit: enriching datasets and quality control (verification of existing attributes). Experiments are carried out on a semantically rich LOD1 dataset of Rotterdam in the Netherlands using 9 attributes. The results are mixed: the accuracy in the estimation of building age depends on the available information used in the regression model. In the best scenario we have achieved predictions with an RMSE of 11 years, but in more realistic situations with limited knowledge about buildings the error is much larger (RMSE = 26 years). Hence the main conclusion of the paper is that inferring building age with 3D city models is possible to a certain extent because it reveals the approximate period of construction, but precise estimations remain a difficult task.

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Mallou, Jesús Varela, Antonio Rial Boubeta et Teresa Braña Tobío. « Consumer Preferences and Brand Equity Measurement of Spanish National Daily Newspapers : A Conjoint Analysis Approach ». Spanish Journal of Psychology 4, n^o 1 (mai 2001) : 48–54. http://dx.doi.org/10.1017/s1138741600005643.

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Brand is a product attribute that, for many types of goods or services, makes a major contribution to consumer preferences. Conjoint analysis is a useful technique for the assessment of brand values for a given consumer or group of consumers. In this paper, an application of conjoint analysis to the estimation of brand values in the Spanish daily newspaper market is reported. Four newspaper attributes were considered: brand (i.e., newspaper name), price (0.60, 1.05, or 1.50 euros), Sunday supplement (yes/no), and daily pullout (yes/no). A total of 510 regular readers of the national press, stratified by age and sex, were asked to rank 16 profiles representing an orthogonal fraction of the possible attribute-level combinations. Brand was by far the most important attribute, whereas price had negligible effect. More generally, the results confirm the utility of conjoint analysis for assessing brand equity in the newspaper market and for estimating the relative importance of the various attributes to different subgroups of consumers.

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Wang, Shengzheng, Dacheng Tao et Jie Yang. « Relative Attribute SVM+ Learning for Age Estimation ». IEEE Transactions on Cybernetics 46, n^o 3 (mars 2016) : 827–39. http://dx.doi.org/10.1109/tcyb.2015.2416321.

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Falade, Ayodele, John Amigun et Florence Oyediran. « PREDICTION OF POROSITY OF RESERVOIR SANDS USING SEISMIC ATTRIBUTES IN “ARIKE” FIELD NIGER DELTA, NIGERIA ». Earth Science Malaysia 6, n^o 2 (2022) : 146–56. http://dx.doi.org/10.26480/esmy.02.2022.146.156.

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The study aimed at predicting the porosity of reservoir sands in ‘Arike field’ Niger Delta, Nigeria by converting seismic trace of the interval of interest in the seismic survey into a porosity log to generate a porosity volume. Optimal number of relevant attributes were selected using multi-attribute analysis. The study discovered that three attributes (energy, velocity fan, and Q factor) were efficient. These attributes were then utilized to train a supervised neural network to establish the relationship between seismic response and porosity. The Opendtect software used, extracted all specified input attributes and target values over the specified range along the well tracks and randomly divided the data into a training and test set attribute. The study established the integration and correlation of energy attribute, velocity fan attribute, and Q factor as relevant seismic attributes for porosity estimation when little or no well log is available, hence giving a means of spatially extending well data.

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Grigorev, Y. A., et O. Yu Pluzhnikova. « ESTIMATION OF ATTRIBUTE VALUES IN JOIN TABLES WHILE OPTIMIZING RELATION-AL DATABASE QUERY ». Informatika i sistemy upravleniya, n^o 1 (2021) : 3–18. http://dx.doi.org/10.22250/isu.2021.67.3-18.

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The article analyzes the problem of estimating join tables cardinality in the process of calculating the cost of relational database query plan. A new algorithm for estimating the distinct values of attributes is proposed. The algorithm allows reducing inaccuracy in cardinality estimation. The consistency of proposed algorithm is proved.

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Augustyn, D. R. « Query-condition-aware V-optimal histogram in range query selectivity estimation ». Bulletin of the Polish Academy of Sciences Technical Sciences 62, n^o 2 (1 juin 2014) : 287–303. http://dx.doi.org/10.2478/bpasts-2014-0029.

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Abstract Obtaining the optimal query execution plan requires a selectivity estimation. The selectivity value allows to predict the size of a query result. This lets choose the best method of query execution. There are many selectivity obtaining methods that are based on different types of estimators of attribute values distribution (commonly they are based on histograms). The adaptive method, proposed in this paper, uses either attribute values distribution or range query condition boundaries one. The new type of histogram - the Query-Conditional-Aware V-optimal one (QCA-V-optimal) - is proposed as a non-parametric estimator of a probability density function of attribute values distribution. This histogram also takes into account information about already processed queries. This information is represented by the 1-dimensional Query Condition Distribution histogram (HQCD) which is an estimator of the include function PI which is also introduced in this paper. PI describes so-called regions of user interest, i.e. it shows how often regions of attribute values domain were used by processed queries. Advantages of the proposed method based on QCA-V-optimal are presented. Conducted experiments reveal small values of a mean relative selectivity estimation error comparing to the error values obtained by methods based on the relevant classical V-optimal histogram and Equi-height one.

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Alvarez, Pedro, Francisco Bolívar, Mario Di Luca et Trino Salinas. « Multiattribute rotation scheme : A tool for reservoir property prediction from seismic inversion attributes ». Interpretation 3, n^o 4 (1 novembre 2015) : SAE9—SAE18. http://dx.doi.org/10.1190/int-2015-0029.1.

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The multiattribute rotation scheme (MARS) is a methodology that uses a numerical solution to estimate a transform to predict petrophysical properties from elastic attributes. This is achieved by estimating a new attribute in the direction of maximum change of a target property in an [Formula: see text]-dimensional Euclidean space formed by an [Formula: see text] number of attributes and subsequent scaling of this attribute to the target unit properties. We have computed the transform from well-log-derived elastic attributes and petrophysical properties, and we have posteriorly applied it to seismically derived elastic attributes. Such transforms can be used to estimate reservoir property volumes for reservoir characterization and delineation in exploration and production settings and to estimate secondary variables in geostatistical workflows for static model generation and reserve estimation. To illustrate the methodology, we applied MARS to estimate a transform to predict the water saturation and total porosity from elastic attributes in a well located in the Barents Sea as well as to estimate a water-saturation volume in a mud-rich turbidite gas reservoir located onshore Colombia.

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Lou, Yihuai, Bo Zhang, Ruiqi Wang, Tengfei Lin et Danping Cao. « Seismic fault attribute estimation using a local fault model ». GEOPHYSICS 84, n^o 4 (1 juillet 2019) : O73—O80. http://dx.doi.org/10.1190/geo2018-0678.1.

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Faults in the subsurface can be an avenue of, or a barrier to, hydrocarbon flow and pressure communication. Manual interpretation of discontinuities on 3D seismic amplitude volume is the most common way to define faults within a reservoir. Unfortunately, 3D seismic fault interpretation can be a time-consuming and tedious task. Seismic attributes such as coherence help define faults, but suffer from “staircase” artifacts and nonfault-related stratigraphic discontinuities. We assume that each sample of the seismic data is located at a potential fault plane. The hypothesized fault divides the seismic data centered at the analysis sample into two subwindows. We then compute the coherence for the two subwindows and full analysis window. We repeat the process by rotating the hypothesized fault plane along a set of user-defined discrete fault dip and azimuth. We obtain almost the same coherence values for the subwindows and the full window if the analysis point is not located at a fault plane. The “best” fault plane results in maximum coherence for the subwindows and minimum coherence for the full window if the analysis point is located at a fault plane. To improve the continuity of the fault attributes, we finally smooth the fault probability attribute along the estimated fault plane. We illustrate the effectiveness of our workflow by applying it to a synthetic and two real seismic data. The results indicate that our workflow successfully produces a continuous fault attribute without staircase artifacts and stratigraphic discontinuities.

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Platov, B. V., R. I. Khairutdinova et A. I. Kadirov. « Evaluating the thickness of thin-bed seams using seismic data on the example of the Tula-Bobrikovian sediments in the Republic of Tatarstan ». Proceedings of higher educational establishments. Geology and Exploration 63, n^o 4 (12 octobre 2021) : 80–87. http://dx.doi.org/10.32454/0016-7762-2020-63-4-80-87.

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Background. Determining the productive deposit thickness is of fundamental importance for assessing the reserves of oil and gas fields. 3D seismic data is used to assess the thickness of seams in the interwell space. However, owing to the limited vertical resolution of seismic data, estimating thicknesses of thin deposits (less than 20 m) is challenging.Aim. To evaluate different approaches to calculating the thickness of the productive deposits based on seismic data with the purpose of selecting the most optimal approach.Materials and methods. We compared the results obtained using different approaches to assessing the productive deposit thickness of the Tula-Bobrikovian age in the interwell space, including the convergence method (calculating the thickness for oilwells with no seismic data used), the use of seismic attributes to calculate the “seismic attribute — reservoir thickness” dependency (for attributes, dominant frequency and mono-frequency component at 60 Hz), estimation of the thickness from the seismic signal shape. Cokriging was used to calculate inferred power maps from seismic attribute data and to classify them by waveform. For each of the techniques, the crossvalidation method and calculating the root-mean-square deviation were used as quality criteria.Results. When assessing the accuracy of thickness map development, the root-mean-square deviation was 12.3 m according to convergence method, 10.2 m — to the dominant frequency attribute, 7.2 m — to the attribute of the monofrequency component at 60 Hz and 6.3 m — to the signal shape classification. The latter method yielded the best results, and the developed thickness map allowed paleo-cut to be traced.Conclusions. Applying the thickness estimation method based on the seismic signal shape allows the value of the root-mean-square deviation to be reduced by a factor of 2 compared to that of the widely adopted convergence method. This approach permits productive deposits thickness to be more accurately estimated and hydrocarbon reserves to be determined.

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Iskandar, Ismed, et Noraini Mohd Razali. « Multi-Mode Failure Models for Attribute Test Data in Reliability Systems, a Bayesian Analysis Approach Using Multi-Nomial Distribution Model ». Advanced Materials Research 903 (février 2014) : 419–24. http://dx.doi.org/10.4028/www.scientific.net/amr.903.419.

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This paper describes the extending model of Multi-mode Failure Models by using the Weibull and Gamma distribution models presented in a conference [1,2]. Different than the models in the previous papers which are for variable test data, in this paper we will describe the use of attribute test data for our model. In reliability theory, the most important problem is to determine the reliability of a complex system from the reliability of its components. The weakness of most reliability theories is that the systems are described and explained as simply functioning or failed. In many real situations, the failures may be from many causes depending upon the age and the environment of the system and its components. Another problem in reliability theory is one of estimating the parameters of the assumed failure models. The estimation may be based on data collected over censored or uncensored life tests. In many reliability problems, the failure data are simply quantitatively inadequate. The Bayesian analyses are more beneficial than classical analyses in such cases. The Bayesian estimation analyses allow us to combine past knowledge or experience in the form of an apriori distribution with life test data to make inferences of the parameter of interest. In this paper, we have investigated the application of the Bayesian estimation analyses to multi-mode failure systems for attribute test data. The cases are limited to the models with independent causes of failure. We select our investigation by using the Multi-nomial distribution as our model. This distribution is widely used in reliability analysis for attribute test data. This model describes the likelihood function and follows with the description of the posterior function. A Beta prior is used in our analysis for each model and it is followed by the estimation of the point, interval, and reliability estimations.

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Zhu, Xiaofeng, Shichao Zhang, Zhi Jin, Zili Zhang et Zhuoming Xu. « Missing Value Estimation for Mixed-Attribute Data Sets ». IEEE Transactions on Knowledge and Data Engineering 23, n^o 1 (janvier 2011) : 110–21. http://dx.doi.org/10.1109/tkde.2010.99.

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Lin, Tengfei, Bo Zhang et Kurt Marfurt. « Geometric seismic attribute estimation using data-adaptive windows ». Interpretation 7, n^o 2 (1 mai 2019) : SC33—SC43. http://dx.doi.org/10.1190/int-2018-0114.1.

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Geometric seismic attributes such as coherence are routinely used for highlighting geologic features such as faults and channels. Traditionally, we use a single user-defined analysis window of fixed size to calculate attributes for the entire seismic volume. In general, smaller windows produce sharper geologic edges, but they are more sensitive to noise. In contrast, larger windows reduce the effect of random noise, but they might laterally smear faults and channel edges and vertically mix the stratigraphy. The vertical and lateral resolutions of a 3D seismic survey change with depth due to attenuation losses and velocity increase, such that a window size that provides optimal images in the shallower section is often too small for the deeper section. A common workaround to address this problem is to compute the seismic attributes using a suite of fixed windows and then splice the results at the risk of reducing the vertical continuity of the final volume. Our proposed solution is to define laterally and vertical smoothly varying analysis windows. The construction of such tapered windows requires a simple modification of the covariance matrix for eigenstructure-based coherence and a less obvious, but also simple, modification of semblance-based coherence. We determine the values of our algorithm by applying it to a vintage 3D seismic survey acquired offshore Louisiana, USA.

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Shu, Xiangbo, Yunfei Cai, Liu Yang, Liyan Zhang et Jinhui Tang. « Computational face reader based on facial attribute estimation ». Neurocomputing 236 (mai 2017) : 153–63. http://dx.doi.org/10.1016/j.neucom.2016.09.110.

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Rietsch, Eike. « A designer attribute for net-sand thickness estimation ». Leading Edge 27, n^o 3 (mars 2008) : 386–93. http://dx.doi.org/10.1190/1.2896631.

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Lin, Chuan-Ju, et Hua-Hua Chang. « Item Selection Criteria With Practical Constraints in Cognitive Diagnostic Computerized Adaptive Testing ». Educational and Psychological Measurement 79, n^o 2 (27 juillet 2018) : 335–57. http://dx.doi.org/10.1177/0013164418790634.

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For item selection in cognitive diagnostic computerized adaptive testing (CD-CAT), ideally, a single item selection index should be created to simultaneously regulate precision, exposure status, and attribute balancing. For this purpose, in this study, we first proposed an attribute-balanced item selection criterion, namely, the standardized weighted deviation global discrimination index (SWDGDI), and subsequently formulated the constrained progressive index (CP_SWDGDI) by casting the SWDGDI in a progressive algorithm. A simulation study revealed that the SWDGDI method was effective in balancing attribute coverage and the CP_SWDGDI method was able to simultaneously balance attribute coverage and item pool usage while maintaining acceptable estimation precision. This research also demonstrates the advantage of a relatively low number of attributes in CD-CAT applications.

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Wang, Erda, Jianhua Wei et Jiawei Zhu. « The effects of improving coastal park attributes on the recreation demand—A case study in Dalian China ». Tourism Economics 23, n^o 1 (21 septembre 2016) : 133–49. http://dx.doi.org/10.5367/te.2015.0503.

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In this study, we use an estimation method of combining revealed preference (RP) with stated preference (SP) to measure the effects of park attribute improvement on the tourism demand and recreation benefits. Three categories of attributes are taken into account including nature, management, and economics. Five model estimations are made using RP, SP, and pooled data, respectively. The random effects negative binomial model is used with the SP data, and the mixed negative binomial model is used with the pooled data. The economic value of each specific park attribute improvement is measured by visitors’ marginal willingness to pay that directly addresses the issues of optimal attribute combinations beyond the observed range and scenarios. We survey a sample of the participants on the sites to obtain both revealed and stated behavior data for trips based on a management-relevant park attribute improvement and different park attribute combinations. Recreation demand models are used to derive total consumer surplus. The results indicate that the congestion reduction in the park has a relatively weak effect on the tourist demand, whereas trash reduction and improved seawater visibility appear to have the most important value to the tourists. Furthermore, the estimated consumer surplus at the present park conditions is about US$85 per recreation trip, and this consumer surplus will increase to US$101 as the quality of the park attribute improved. The overall park attribute improvement will increase recreational trip by 1.22 times per person per year from the present condition, and the net increase in consumer surplus reaches to US$205 per participant per year as a result of overall park attribute improvement.

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Minamino, Yuka, Shinji Inoue et Shigeru Yamada. « Multi-Attribute Utility Theory for Estimation of Optimal Release Time and Change-Point ». International Journal of Reliability, Quality and Safety Engineering 22, n^o 04 (août 2015) : 1550019. http://dx.doi.org/10.1142/s0218539315500199.

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Existing optimal software release problems have been discussed by using an evaluation criterion such as cost, reliability, delivery. When we use the methods by those evaluation criteria, the optimal release time is determined by an evaluation criterion. However, it is more realistic that we determine the optimal release time with multiple attributes. Therefore, in this study, we estimate the optimal release time by using multi-attribute utility theory (MAUT). Since MAUT is one of utility theories, we can estimate an optimal release time and change-point from the perspective of utility by maximizing the multi-attribute utility function. Especially, we consider the both of two attributes: cost and reliability. Then, we apply a software reliability growth model (SRGM) with change-point to represent the cost and reliability attributes. Concretely, we use an exponential SRGM with change-point. That is, we can estimate not only the optimal release time but also change-point. Finally, we show numerical examples by using actual data sets. Especially, we check the behavior of the optimal release time, change-point, total software cost and utility.

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Analytis, Pantelis P., Amit Kothiyal et Konstantinos Katsikopoulos. « Multi-attribute utility models as cognitive search engines ». Judgment and Decision Making 9, n^o 5 (septembre 2014) : 403–19. http://dx.doi.org/10.1017/s1930297500006781.

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AbstractIn optimal stopping problems, decision makers are assumed to search randomly to learn the utility of alternatives; in contrast, in one-shot multi-attribute utility optimization, decision makers are assumed to have perfect knowledge of utilities. We point out that these two contexts represent the boundaries of a continuum, of which the middle remains uncharted: How should people search intelligently when they possess imperfect information about the alternatives? We assume that decision makers first estimate the utility of each available alternative and then search the alternatives in order of their estimated utility until expected benefits are outweighed by search costs. We considered three well-known models for estimating utility: (i) a linear multi-attribute model, (ii) equal weighting of attributes, and (iii) a single-attribute heuristic. We used 12 real-world decision problems, ranging from consumer choice to industrial experimentation, to measure the performance of the three models. The full model (i) performed best on average but its simplifications (ii and iii) also had regions of superior performance. We explain the results by analyzing the impact of the models’ utility order and estimation error.

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Wan Norhidayah, W. Mohamad, Mzek Tareq, Samdin Zaiton et Hasan-Basri Bakti. « Exploring Preference Heterogeneity and Willingness to Pay for Tourist Facility Attributes in Kenyir Lake, Malaysia ». Malaysian Journal of Economic Studies 59, n^o 2 (26 décembre 2022) : 241–60. http://dx.doi.org/10.22452/mjes.vol59no2.4.

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Natural environments with great recreational value can potentially support tourism, conserve natural areas and prevent environmental damage. However, visiting recreational destinations depends on several motivations related to visitors’ preferences. Therefore, this study aims to identify the heterogeneity of Kenyir Lake, Malaysia, visitors’ preferences and their willingness to pay (WTP) for tourist facility attributes. The choice experiment method has been widely used for economic valuation, and the latent class model can capture the heterogeneity of preferences by characterising respondents in various groups of preferences. The outcome of this study revealed heterogeneous preferences in two classes. The first class represented 91.3% and the second class represented 8.7% of respondents. Moreover, this study’s respondents showed positive WTP values for most attributes. However, the most preferred attribute was superior toilet service, with a WTP estimation of RM7.63 in Class 1. While in Class 2, the most preferred attribute was new jetty construction with a WTP estimation of RM1.99. The findings of this study can assist policymakers in implementing more effective policies as it has provided detailed information about visitors’ preferences for tourist facility attributes in Malaysia.

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Gupta, Viral, P. K. Kapur et Deepak Kumar. « Prioritizing and Optimizing Disaster Recovery Solution using Analytic Network Process and Multi Attribute Utility Theory ». International Journal of Information Technology & ; Decision Making 18, n^o 01 (janvier 2019) : 171–207. http://dx.doi.org/10.1142/s0219622018500372.

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Selection of a suitable disaster recovery solution is an essential activity performed in an enterprise to facilitate recovery of critical business functions and Information Technology (IT) systems within a tolerable time limit known as disaster recovery time (DRT). The estimation of optimal DRT plays a significant role in IT as it influences overall costs required to ensure business continuity. The estimation of optimal DRT depends upon the capabilities of a chosen disaster recovery solution and multiple conflicting attributes. This paper presents an integrated approach to selecting the best disaster recovery solution using analytic network process (ANP) and estimating optimal DRT using Multi-Attribute Utility Theory (MAUT). ANP is applied to determine the best disaster recovery solution using seven criteria: people, recovery objectives, security, technology, cost, site infrastructure, and regulatory. MAUT estimates the optimal DRT for the best disaster recovery solution based on three conflicting attributes: cost, reliability, and processed backlog transactions. The proposed approach applies to an enterprise application in the banking sector and this paper tests its effectiveness by comparing the results from four different enterprises. This study offers valuable insights to the disaster recovery practitioner to select the best disaster recovery solution and to estimate optimal DRT.

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Runje, Biserka, Živko Kondić, Amalija Horvatić Novak et Zdenka Keran. « Estimation of process capability based on continuous and attribute data ». Tehnički glasnik 13, n^o 2 (17 juin 2019) : 162–64. http://dx.doi.org/10.31803/tg-20190514132701.

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In the paper, a process capability and process performance analyses for continuous and attribute data are conducted by using an industry example. The inner diameter of the bearing rings is analysed by applying the normal capability analysis for the continuous data and by applying the binomial capability analysis for the attribute data. In order to quantify the process performance and process capability for continuous and attribute data, the sigma level or the process sigma is calculated. This represents an alternative method to calculating process capability indices and process performance indices.

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Land, Margaret, Sarjinder Singh et Stephen A. Sedory. « Estimation of a rare sensitive attribute using Poisson distribution ». Statistics 46, n^o 3 (juin 2012) : 351–60. http://dx.doi.org/10.1080/02331888.2010.524300.

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Tian, Qing, et Songcan Chen. « Cumulative attribute relation regularization learning for human age estimation ». Neurocomputing 165 (octobre 2015) : 456–67. http://dx.doi.org/10.1016/j.neucom.2015.03.078.

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Li, Feng, Shu-Ren Zhou, Jian-Ming Zhang, Deng-Yong Zhang et Ling-Yun Xiang. « Attribute-based knowledge transfer learning for human pose estimation ». Neurocomputing 116 (septembre 2013) : 301–10. http://dx.doi.org/10.1016/j.neucom.2012.05.032.

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Harte, Johanna M., Pieter Koele et Gijsbert van Engelenburg. « Estimation of attribute weights in a multiattribute choice situation ». Acta Psychologica 93, n^o 1-3 (septembre 1996) : 37–55. http://dx.doi.org/10.1016/0001-6918(96)00021-2.

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Burhannudinnur, Muhammad, et Suryo Prakoso. « A Simple Method for P-waves Velocity Estimation Using Pore Attributes Shape Factor and Tortuosity ». Aceh International Journal of Science and Technology 9, n^o 2 (7 septembre 2020) : 83–89. http://dx.doi.org/10.13170/aijst.9.2.17549.

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Several researchers have arranged an approach to estimating the P-wave velocity, but none of them specifically relates to the pore attribute. Pore attributes are one of the main factors that affect pore complexity and rock quality. If P-wave velocity is influenced by the pore complexity, then it should be possible to arrange a simple relationship of P-wave velocity with the pore attribute. This study is intended to construct an empirical relationship of P-wave velocity with a combination of pore attributes, shape factor, and tortuosity (Fsτ) so that the P-wave velocity can be easily estimated. This study used two sandstone datasets from 2 different basins, which are the northern part of the West Java basin and the Kutai basin. This research shows that a simple empirical equation can be arranged to relate the P-wave velocity with Fsτ. This relationship provides a good correlation coefficient. It offers an easy and straightforward approach to estimating P-wave

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Scheevel, J. R., et K. Payrazyan. « Principal Component Analysis Applied to 3D Seismic Data for Reservoir Property Estimation ». SPE Reservoir Evaluation & ; Engineering 4, n^o 01 (1 février 2001) : 64–72. http://dx.doi.org/10.2118/69739-pa.

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Summary We apply a common statistical tool, Principal Component Analysis (PCA) to the problem of direct property estimation from three-dimensional (3D) seismic-amplitude data. We use PCA in a novel way to successfully make detailed effective porosity predictions in channelized sand and shale. The novelty of this use revolves around the sampling method, which consists of a small vertical sampling window applied by sliding along each vertical trace in a cube of seismic-amplitude data. The window captures multiple, vertically adjacent amplitude samples, which are then treated as vectors for purposes of the PCA analysis. All vectors from all sample window locations within the seismic-data volume form the set of input vectors for the PCA algorithm. Final output from the PCA algorithm can be a cube of assigned classes, whose clustering is based on the values of the most significant principal components (PC's). The clusters are used as a categorical variable when predicting reservoir properties away from well control. The novelty in this approach is that PCA analysis is used to analyze covariance relationships between all vector elements (neighboring amplitude values) by using the statistical mass of the large number of vectors sampled in the seismic data set. Our approach results in a powerful signal-analysis method that is statistical in nature. We believe it offers data-driven objectivity and a potential for property extraction not easily achieved in model-driven fourier-based time-series methods of analysis (digital signal processing). We evaluate the effectiveness of our method by applying a cross-validation technique, alternately withholding each of the three wells drilled in the area and computing predicted effective porosity (PHIE) estimates at the withheld location by using the remaining two wells as hard data. This process is repeated three times, each time excluding only one of the wells as a blind control case. In each of the three blind control wells, our method predicts accurate estimates of sand/shale distribution in the well and effective porosity-thickness product values. The method properly predicts a low sand-to-shale ratio at the blind well location, even when the remaining two hard data wells contain only high sand-to-shale ratios. Good predictive results from this study area make us optimistic that the method is valuable for general reservoir property prediction from 3D seismic data, especially in areas of rapid lateral variation of the reservoir. We feel that this method of predicting properties from the 3D seismic is preferable to traditional, solely variogram-based geostatistical estimation methods. Such methods have difficulty capturing the detailed lithology distribution when limited by the hard data control's sampling bias. This problem is especially acute in areas where rapid lateral geological variation is the rule. Our method effectively overcomes this limitation because it provides a deterministic soft template for reservoir-property distributions. Introduction Reservoir Prediction from Seismic. The use of the reflection seismic-attribute data for the prediction of detailed reservoir properties began at least as early as 1969.1 Use of seismic attributes for reservoir prediction has accelerated in recent years, especially with the advent of widely available high-quality 3D seismic data. In practice, a seismic attribute is any property derived from the seismic reflection (amplitude) signal during or after final processing. Any attributes may be compared with a primary reservoir property or lithology in an attempt to devise a method of attribute-guided prediction of the primary property away from well control. The prediction method can vary from something as simple as a linear multiplier (single attribute) to multi-attribute analysis with canonical correlation techniques,2 geostatistical methods,3 or fully nonlinear, fuzzy methods.4 The pace of growth in prediction methodologies using seismic attributes seems to be outpaced only by the proliferation in the number and types of seismic attributes reported in the literature.5 As more researchers find predictive success with one or more new attributes, the list of viable reservoir-predictive attributes continues to grow. Chen and Sidney6 have cataloged more than 60 common seismic attributes along with a description of their apparent significance and use. Despite the rich history of seismic attribute in reservoir prediction, the practice remains difficult and uncertain. The bulk of this uncertainty arises from the unclear nature of the physics connecting many demonstrably useful attributes to a corresponding reservoir property. Because of the complex and varied physical processes responsible for various attributes, the unambiguous use of attributes for direct reservoir prediction will likely remain a challenge for years to come. In addition to the questions about the physical origin of some attributes, there is the possibility of encountering statistical pitfalls while using multiple attributes for empirical reservoir-property prediction. For example, it has been demonstrated that as the number of attributes used in an evaluation increases, the potential arises that one or more attributes will produce a false correlation with well data.7 Also, many attributes are derived with similar signal-processing methods and can, in some cases, be considered largely redundant with respect to their seismic-signal description. Lendzionowski et al.8 maintain that the maximum number of independent attributes required to fully describe a trace segment is a quantity 2BT, where B=bandwidth (Hz) and T=trace-segment length (sec). If this is supportable, it suggests that most of the more common attributes are at least partially redundant. The danger of such redundancy is that it falsely enhances statistical correlation with the well property. Doing so may suggest that many seemingly independent seismic attributes display similar well-property trends. Finally, the use of a particular approach with attributes involves some subjectivity and prior experience on the part of the practitioner to be successful and reproducible. This is a source of potential error that cannot be quantified but also, in most cases, cannot be avoided. The most successful workers in the field of reservoir prediction from seismic, not coincidentally, are also the most experienced in the field.

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Zhou, Zhangkai, et Yihan Li. « Parameter Estimation of Attribute Scattering Center Based on Water Wave Optimization Algorithm ». Scientific Programming 2021 (7 septembre 2021) : 1–6. http://dx.doi.org/10.1155/2021/6733510.

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For the problem of attribute scattering center parameter estimation in synthetic aperture radar (SAR) image, a method based on the water wave optimization (WWO) algorithm is proposed. First, the segmentation and decoupling of high-energy regions in SAR image are performed in the image domain to obtain the representation of a single scattering center. Afterwards, based on the parameterized model of the attribute scattering center, an optimization problem is constructed to search for the optimal parameters of the separated single scattering center. In this phase, the WWO algorithm is introduced to optimize the parameters. The algorithm has powerfully global and local searching capabilities and avoids falling into local optimum while ensuring the optimization accuracy. Therefore, the WWO algorithm could ensure the reliability of scattering center parameter estimation. The single scattering center after solution is eliminated from the original image and the residual image is segmented into high-energy regions, so the parameters of the next scattering center are estimated sequentially. Finally, the parameter set of all scattering centers in the input SAR image can be obtained. In the experiments, firstly, the parameter estimation verification is performed based on the SAR images in the MSTAR dataset. The comparison of the parameter estimation results with the original image and the reconstruction based on the estimated parameter set reflect the effectiveness of the proposed method. In addition, the experiment is also conducted using the SAR target recognition algorithms based on the estimated attribute parameters. By comparing the recognition performance with other parameter estimation algorithms under the same conditions, the performance superiority of the proposed method in attribute scattering center parameter estimation is further demonstrated.

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Nguyen, Hoa-Hung, Jae-Hyun Park et Han-You Jeong. « A Simultaneous Pipe-Attribute and PIG-Pose Estimation (SPPE) Using 3-D Point Cloud in Compressible Gas Pipelines ». Sensors 23, n^o 3 (20 janvier 2023) : 1196. http://dx.doi.org/10.3390/s23031196.

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An accurate estimation of pipe attributes, pose of pipeline inspection gauge (PIG), and downstream pipeline topology is essential for successful in-line inspection (ILI) of underground compressible gas pipelines. Taking a 3D point cloud of light detection and ranging (LiDAR) or time-of-flight (ToF) camera as the input, in this paper, we present the simultaneous pipe-attribute and PIG-pose estimation (SPPE) approach that estimates the optimal pipe-attribute and PIG-pose parameters to transform a 3D point cloud onto the inner pipe wall surface: major- and minor-axis lengths, roll, pitch, and yaw angles, and 2D deviation from the center of the pipe. Since the 3D point cloud has all spatial information of the inner pipe wall measurements, this estimation problem can be modeled by an optimal transformation matrix estimation problem from a PIG sensor frame to the global pipe frame. The basic idea of our SPPE approach is to decompose this transformation into two sub-transformations: The first transformation is formulated as a non-linear optimization problem whose solution is iteratively updated by the Levenberg–Marquardt algorithm (LMA). The second transformation utilizes the gravity vector to calculate the ovality angle between the geometric and navigation pipe frames. The extensive simulation results from our PIG simulator based on the robot operating system (ROS) platform demonstrate that the proposed SPPE can estimate the pipe attributes and PIG pose with excellent accuracy and is also applicable to real-time and post-processing non-destructive testing (NDT) applications thanks to its high computational efficiency.

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Zagorskas, Jurgis, et Zenonas Turskis. « MULTI‐ATTRIBUTE MODEL FOR ESTIMATION OF RETAIL CENTRES INFLUENCE ON THE CITY STRUCTURE. KAUNAS CITY CASE STUDY ». Technological and Economic Development of Economy 12, n^o 4 (31 décembre 2006) : 347–52. http://dx.doi.org/10.3846/13928619.2006.9637765.

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This paper concerns multi‐attribute decision support methodology applied to analyze the impact of retail centres on the city as a complex system. Influence on the city is described as the sum of effects the retail centres give to the quality of life to the neighborhood and other city population, the work of city transportation system, architectural and urban perception of the city. The gamut of impact is estimated and numerical expression is obtained. Using it different alternative objects are compared. The task is described by many attributes. The main attributes are distinguished to measure the influence of retail centres on quality of life, the work of transportation system, on the economics and the architectural ‐ urban perception of the city. On the basis of expert judgment the weights of attributes are estimated. The normalization of the efficiency attributes is done using linear normalization method. The values of different attributes are derived from the rating done by urbanism ground experts. From ideal values the optimal alternative is made. Influence is estimated using Multiplicative Summarized Optimal Criterion method. The strategy of retail centres development is defined by comparison of existing objects to ideal value. The multi‐attribute model for estimation of retail centres influence to the city was used in “Kaunas city municipality specialised plan for dislocation of retail centres”. The research results determined effectiveness of existing objects and future development strategy.

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Choi, Jong Woo, Chengyan Yue, James Luby, Shuoli Zhao, Karina Gallardo, Vicki McCracken et Jim McFerson. « Estimation of market equilibrium values for apple attributes ». China Agricultural Economic Review 10, n^o 1 (5 février 2018) : 135–51. http://dx.doi.org/10.1108/caer-12-2016-0192.

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Purpose Development of new cultivars requires extensive genetic knowledge, trained personnel, and significant financial resources, so it is crucial for breeders to focus on the attributes most preferred by the key supply chain stakeholders such as consumers and producers. The purpose of this paper is to identify which attributes generate the highest total revenue or social surplus, information that breeders can take into account as they allocate resources to focus on attributes in their breeding programs. Design/methodology/approach This study used mail-in and online surveys to collect consumer and producer choice experiment data, and then employed mixed logit models to analyze and simulate individual producer and consumer willingness to pay (WTP) for the apple attributes. Findings Based on the simulation results, this study derived the supply and demand curves and the market equilibrium prices and quantities for each apple attribute. Based on the WTP analysis for both consumer and producer, this paper found the highest equilibrium price and welfare for apples come from crispness, followed by flavor. Originality/value The authors propose a framework to estimate the equilibrium prices and quantities of a product based on the results of choice experiments. The framework can be easily adapted to understand any countries’ producer and consumer preferences for certain products.

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Azaïs, Romain, Bernard Delyon et François Portier. « Integral estimation based on Markovian design ». Advances in Applied Probability 50, n^o 3 (septembre 2018) : 833–57. http://dx.doi.org/10.1017/apr.2018.38.

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AbstractSuppose that a mobile sensor describes a Markovian trajectory in the ambient space and at each time the sensor measures an attribute of interest, e.g. the temperature. Using only the location history of the sensor and the associated measurements, we estimate the average value of the attribute over the space. In contrast to classical probabilistic integration methods, e.g. Monte Carlo, the proposed approach does not require any knowledge of the distribution of the sensor trajectory. We establish probabilistic bounds on the convergence rates of the estimator. These rates are better than the traditional `rootn'-rate, wherenis the sample size, attached to other probabilistic integration methods. For finite sample sizes, we demonstrate the favorable behavior of the procedure through simulations and consider an application to the evaluation of the average temperature of oceans.

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Singh, Rajesh, Mukesh Kumar et Housila P. Singh. « ON ESTIMATION OF POULATION MEAN USING INFORMATION ON AUXILIARY ATTRIBUTE ». Pakistan Journal of Statistics and Operation Research 9, n^o 4 (6 février 2014) : 363. http://dx.doi.org/10.18187/pjsor.v9i4.211.

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Han, Hu, Anil K. Jain, Fang Wang, Shiguang Shan et Xilin Chen. « Heterogeneous Face Attribute Estimation : A Deep Multi-Task Learning Approach ». IEEE Transactions on Pattern Analysis and Machine Intelligence 40, n^o 11 (1 novembre 2018) : 2597–609. http://dx.doi.org/10.1109/tpami.2017.2738004.

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Padmavathy, T. V., M. N. Vimalkumar et N. Sivakumar. « Region-specific multi-attribute white mass estimation-based mammogram classification ». Personal and Ubiquitous Computing 22, n^o 5-6 (3 avril 2018) : 1093–98. http://dx.doi.org/10.1007/s00779-018-1135-4.

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Tugnait, Jitendra K. « Deviance Tests for Graph Estimation From Multi-Attribute Gaussian Data ». IEEE Transactions on Signal Processing 68 (2020) : 5632–47. http://dx.doi.org/10.1109/tsp.2020.3023575.

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Jing, Tao, Hongyan Huang, Yue Wu, Qinghe Gao, Yan Huo et Jiayu Sun. « Threshold-free multi-attributes physical layer authentication based on expectation–conditional maximization channel estimation in Internet of Things ». International Journal of Distributed Sensor Networks 18, n^o 7 (juillet 2022) : 155013292211078. http://dx.doi.org/10.1177/15501329221107822.

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With the number of Internet of Things devices continually increasing, the endogenous security of Internet of Things communication systems is growingly critical. Physical layer authentication is a powerful means of resisting active attacks by exploiting the unique characteristics inherent in wireless signals and physical devices. Many existing physical layer authentication schemes usually assume physical layer attributes obey certain statistical distributions that are unknown to receivers. To overcome the uncertainty, machine learning–based authentication approaches have been employed to implement threshold-free authentication. In this article, we utilize an expectation–conditional maximization algorithm to provide the physical layer attribute estimates required for the authentication phase and a logistic regression model to achieve threshold-free physical layer authentication. Moreover, a Frank–Wolfe algorithm is considered to achieve fast convergence of the logistic regression parameters and multi-attributes are adopted to increase the differentiation of transmitters. Simulation results demonstrate that the obtained attribute estimates are sufficient to provide a reliable source of data for authentication and the proposed threshold-free multi-attributes physical layer authentication scheme can effectively improve authentication accuracy, with the false alarm rate P f reduced to 0.0263% and the miss detection rate P m reduced to 0.3466%.

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