Search results

Assessing the failure probability of engineering structures is still a challenging task in the presence of various uncertainties due to the involvement of expensive-to-evaluate computational models. The traditional simulation-based approaches require tremendous computational effort, especially when the failure probability is small. Thus, the use of more efficient surrogate modeling techniques to emulate the true performance function has gained increasingly more attention and application in recent years. In this paper, an active learning method based on a Kriging model is proposed to estimate the failure probability with high efficiency and accuracy.

To effectively identify informative samples for the enrichment of the design of experiments, a set of new learning functions is proposed. These learning functions are successfully incorporated into a sampling scheme, where the candidate samples for the enrichment are uniformly distributed in the n-dimensional hypersphere with an iteratively updated radius. To further improve the computational efficiency, a parallelization strategy that enables the proposed algorithm to select multiple sample points in each iteration is presented by introducing the K-means clustering algorithm. Hence, the proposed method is referred to as the adaptive Kriging method based on K-means clustering and sampling in n-Ball (AK-KBⁿ).

The performance of AK-KBⁿ is evaluated through several numerical examples. According to the generated results, all the proposed learning functions are capable of guiding the search toward sample points close to the LSS in the critical region and result in a converged Kriging model that perfectly matches the true one in the regions of interest. The AK-KBⁿ method is demonstrated to be well suited for structural reliability analysis and a very good performance is observed in the investigated examples.

In this study, the statistical information of Kriging prediction, the relative contribution of the sample points to the failure probability and the distances between the candidate samples and the existing ones are all integrated into the proposed learning functions, which enables effective selection of informative samples for updating the Kriging model. Moreover, the number of required iterations is reduced by introducing the parallel computing strategy, which can dramatically alleviate the computation cost when time demanding numerical models are involved in the analysis.

As existing studies show the accuracy of sampling methods depends heavily on the evaluation metric in web accessibility evaluation, the purpose of this paper is to propose a sampling method OPS-WAQM optimized for Web Accessibility Quantitative Metric (WAQM). Furthermore, to support quick accessibility evaluation or real-time website accessibility monitoring, the authors also provide online extension for the sampling method.

In the OPS-WAQM method, the authors propose a minimal sampling error model for WAQM and use a greedy algorithm to approximately solve the optimization problem to determine the sample numbers in different layers. To make OPS-WAQM online, the authors apply the sampling in crawling strategy.

The sampling method OPS-WAQM and its online extension can both achieve good sampling quality by choosing the optimal sample numbers in different layers. Moreover, the online extension can also support quick accessibility evaluation by sampling and evaluating the pages in crawling.

To the best of the authors’ knowledge, the sampling method OPS-WAQM in this paper is the first attempt to optimize for a specific evaluation metric. Meanwhile, the online extension not only greatly reduces the serious I/O issues in existing web accessibility evaluation, but also supports quick web accessibility evaluation by sampling in crawling.

This paper aims to solve the problems of big data analytics for prediction including volume, veracity and velocity by improving the prediction result to an acceptable level and in the shortest possible time.

This paper is divided into two parts. The first one is to improve the result of the prediction. In this part, two ideas are proposed: the double pruning enhanced random forest algorithm and extracting a shared learning base from the stratified random sampling method to obtain a representative learning base of all original data. The second part proposes to design a distributed architecture supported by new technologies solutions, which in turn works in a coherent and efficient way with the sampling strategy under the supervision of the Map-Reduce algorithm.

The representative learning base obtained by the integration of two learning bases, the partial base and the shared base, presents an excellent representation of the original data set and gives very good results of the Big Data predictive analytics. Furthermore, these results were supported by the improved random forests supervised learning method, which played a key role in this context.

All companies are concerned, especially those with large amounts of information and want to screen them to improve their knowledge for the customer and optimize their campaigns.

The purpose of this paper is to propose an approach regarding the management measures for solving environmental problems in protected areas. Two neighboring protected areas with different features were chosen in order to investigate the similarity of the environmental problems with regard to these two areas and if it is possible for these problems to be solved through a network of protected areas.

The research was carried out through the use of a questionnaire which was distributed to the inhabitants and visitors of both areas, as well as through interviews with the representatives of organizations responsible for the management and administration of those areas and representatives of the municipalities and the regional authorities these two national parks belong to. Simple random sampling was applied to the inhabitants and cluster sampling to the visitors.

The results show that with regard to the visitors the most important problem is illegal hunting while for the inhabitants equally important is the problem of pollution and cleanliness. However, those responsible with the management of the two National Parks think that the greatest threat to the wider area is the problem of floods.

The views of the stakeholders in a given time, provides us with the best possible information for solving the problems faced and can be used as a tool for increasing the effectiveness of the measures which have been taken to deal with the particular problems.

Cluster analysis was used on three files of citations from social science journals to other journals. The files were a pilot study, a file of criminology data and a very large file covering all social sciences. The criminology data was divided into sections drawn from 1950, 1960, and 1970 sources. The large file was in two sections, one drawn from a ranked list of source journals and the other from a list of journals selected at random. The study looked at the effect of several cluster methods and various ways of normalizing the data to find out which observed effects are true properties of the data. The results indicated that clusters of social science journals generated using citations have a non‐hierarchical structure. The criminology samples from 1960 and 1970 showed little change over ten years in the main clusters, but the two sections of the large file gave results which, although similar in general shape, differed substantially in their details. The overall conclusion is that cluster analysis is an unsuitable approach to the design of secondary services in the social sciences, though it may have some value in automatic retrieval systems. Two problems are the vast amounts of data needed and the difficulty of presenting results comprehensibly, particularly with overlapping clusters.

The purpose of this paper is to investigate the effect of service quality toward public satisfaction and public trust on local government in Indonesia.

A quantitative approach was used to achieve the objectives of the study. The study consisted of nine exogenous variables and one endogenous variable. The exogenous variables were delivery, timeliness, information, professionalism, staff attitude, organizational politics, internal roles, external roles and citizen satisfaction, while the endogenous variable was public trust toward the regional government. The samples were taken using two sampling methods, cluster sampling technique and proportional stratified random sampling technique. The cluster sampling was institutional sample in which the researchers selected 2 out of 11 regencies in West Sumatra, and 2 out of 7 cities in West Sumatra. The regencies were Pasaman Barat and Tanah Datar and the cities were Padang Panjang and Padang. In the lower level, there were ten nagari and ten lurah. On the second stage, the sample was selected using the proportional stratified random sampling technique that had been set at the first stage. Slovin formula with 2 percent of errors was used to determine the number of samples. The total respondents in this study were 4,177 respondents.

The hypothetical model can be used as a new model for public service that was provided by the local governments (cities and districts) and it was able to increase citizen satisfaction and citizen trust with local government, especially in the basic need services such as education and health as described in Figure 1. In order to increase public satisfaction on the basic needs, such as education and health services, regional government should improve delivery, timeliness of service, availability of information, staff professionalism, staff attitude, external and internal roles of manager and at the same time minimize organizational politics within the local government. Furthermore, it is also evident in this model that increasing public satisfaction on basic services can increase public confidence toward regional government. The finding that shows the novelty of this research is the internal and external role of managers in improving public satisfaction and trust in regional government. Meanwhile, the addition of internal political as variable is a development to improve the existing models.

Regional government should reconstruct their basic public service in order to meet need of the public. No previous study has comprehensively studied the relationship between interaction quality, physical environmental quality, and outcome quality to public satisfaction and its implication to public trust, especially in Indonesia.

This paper aims to overcome the defect that the traditional clustering method is excessively dependent on initial clustering radius and also provide new technical measures for detecting the component content of lubricating oil based on the fuzzy neural system model.

According to the layers model of the fuzzy neural system model for the given sample data pair, the new clustering method can be implemented, and through the fuzzy system model, the detection method for the selected oil samples is given. By applying this method, the composition contents of 30 kinds of oil samples in lubricating oil are checked, and the actual composition contents of oil samples are compared.

Through the detection of 21 mineral elements in 30 oil samples, it can be known that the four mineral elements such as Zn, P, Ca and Mg have largest contribution rate to the lubricating oil, and they can be regarded as the main factors for classification of lubricating oil. The results show that the fuzzy system to be established based on sample data clustering has better performance in detection lubricant component content.

In spite of lots of methods for detecting the component of lubricating oil at the present, there is still no detection of the component of lubricating oil through clustering method based on sample data pair. The new nearest clustering method is proposed in this paper, and it can be more effectively used to detect the content of lubricating oil.

The purpose this paper is to review some of the statistical methods used in the field of social sciences.

A review of some of the statistical methodologies used in areas like survey methodology, official statistics, sociology, psychology, political science, criminology, public policy, marketing research, demography, education and economics.

Several areas are presented such as parametric modeling, nonparametric modeling and multivariate methods. Focus is also given to time series modeling, analysis of categorical data and sampling issues and other useful techniques for the analysis of data in the social sciences. Indicative references are given for all the above methods along with some insights for the application of these techniques.

This paper reviews some statistical methods that are used in social sciences and the authors draw the attention of researchers on less popular methods. The purpose is not to give technical details and also not to refer to all the existing techniques or to all the possible areas of statistics. The focus is mainly on the applied aspect of the techniques and the authors give insights about techniques that can be used to answer problems in the abovementioned areas of research.

Feature space heterogeneity exists widely in various application fields of classification techniques, such as customs inspection decision, credit scoring and medical diagnosis. This paper aims to study the relationship between feature space heterogeneity and classification performance.

A measurement is first developed for measuring and identifying any significant heterogeneity that exists in the feature space of a data set. The main idea of this measurement is derived from a meta-analysis. For the data set with significant feature space heterogeneity, a classification algorithm based on factor analysis and clustering is proposed to learn the data patterns, which, in turn, are used for data classification.

The proposed approach has two main advantages over the previous methods. The first advantage lies in feature transform using orthogonal factor analysis, which results in new features without redundancy and irrelevance. The second advantage rests on samples partitioning to capture the feature space heterogeneity reflected by differences of factor scores. The validity and effectiveness of the proposed approach is verified on a number of benchmarking data sets.

Measurement should be used to guide the heterogeneity elimination process, which is an interesting topic in future research. In addition, to develop a classification algorithm that enables scalable and incremental learning for large data sets with significant feature space heterogeneity is also an important issue.

Measuring and eliminating the feature space heterogeneity possibly existing in the data are important for accurate classification. This study provides a systematical approach to feature space heterogeneity measurement and elimination for better classification performance, which is favorable for applications of classification techniques in real-word problems.

A measurement based on meta-analysis for measuring and identifying any significant feature space heterogeneity in a classification problem is developed, and an ensemble classification framework is proposed to deal with the feature space heterogeneity and improve the classification accuracy.

The purpose of this paper is to examine how business ecosystems evolve, what is the identity of business ecosystem and is the ecosystem identity static or dynamics. To understand the above questions, this paper is conducted on stone carving clusters in India.

The author engaged the ethnographic approach in this study. To sample stone carving clusters of India, the author followed the snowball sampling method. Further, the author did collect the information by informal personal discussions, focus group discussions and participant observations. Furthermore, the thematic analysis and interpretative phenomenological analysis were applied to process the data. The validity and reliability of the method was ascertained by testing the credibility, dependability, confirmability and transferability.

The author found that the business ecosystem of stone carving was dynamic, and it was transformed from the buyer-driven ecosystem to the supplier-driven ecosystem. The identities of the early stage business ecosystem and the late stage ecosystem were analyzed through product, network and information flow. The author developed a structural framework to conceptualize the identity domain of the business ecosystem and the author named it as “nature-conduct-performance model.” Also, the author conceptualized the identity evolution, the influence of social system on business ecosystem identity, and identity-based conflicts and identity-based cooperation in the stone carving business ecosystem.

This study is making additional theoretical contribution in conceptualize the business ecosystem from the identity construct.