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The sufficient utilization of the mega data acquired from operative power units is a promising way leading to high efficient, clean and safe operation of power plant. Hereinto, data filtering is a critical link to the data-driven dynamic model identification aiming at optimizing the process control. The machine learning is an advantageous approach for filtering usable data from mega databases for identification due to its effective statistical learning strategy to the field data with noises, disturbances and coupling quantities. Therefore, a data filtering method of combining Gaussian Naive Bayesian classifier and prediction error method (Gaussian NB-PEM) is proposed in this paper. Firstly, variables associated with identification model are selected by analyzing the characteristics of the process. Secondly, the GaussianNB classifier is used for coarse data filtering by calculating the priori probability of each attribute from training sample set and the probability with all possible values of the known categories for testing sample set. Thirdly, the prediction error method is used for further data filtering based on model fitting. By using the filtered closed-loop data, the dynamic characteristics of the superheated steam temperature is modeled and verified by closed-loop control simulation, showing the validity of the Gaussian NB-PEM data filtering method.

Heterogeneous, distributed multi-databases (data warehouses, grids) are growing day by day as businesses demand more economical and flexible solutions for their data storage and processing needs. One of the main issues obstructing the large scale deployment of distributed multi-databases is latencies involved in data retrieval. Such problems are usually solved with the help of persistent caches. The paper presents a semantic caching architecture which supports faster semantic matching in query processing. This paper enhances the existing architecture of semantic cache and provides the mapping of hierarchical indexing scheme over it with the help of case study in order to represent the working of architecture. This paper also provides experimental results of the query processing over updated architecture and provides the discussion over comparison of the extended architecture with the previously proposed architecture.

Contact Logics provide a natural framework for representing and reasoning about regions in several areas of computer science. In this paper, we focus our attention on reasoning methods for Contact Logics and address the satisfiability problem and the unifiability problem. Firstly, we give sound and complete tableaux-based decision procedures in Contact Logics and we obtain new results about the decidability/complexity of the satisfiability problem in these logics. Secondly, we address the computability of the unifiability problem in Contact Logics and we obtain new results about the unification type of the unifiability problem in these logics.

A common task of Web users is querying structured information from Web pages. In this paper we propose a novel query processor for systematically discovering any-k relations from Web search results with conjunctive queries. The 'any-k' phrase denotes that retrieved tuples are not ranked by the system.For realizing this interesting scenario the query processor transfers a structured query into keyword queries that are submitted to a search engine, forwards search results to relation extractors, and then combines relations into result tuples.Unfortunately, relation extractors may fail to return a relation for a result tuple. We propose a solid information theory-based approach for retrieving missing attribute values of partially retrieved relations. Moreover, user-defined data sources may not return at least k complete result tuples. To solve this problem, we extend the Eddy query processing mechanism [14] for our 'querying the Web' scenario with a continuous, adaptive routing model. The model determines the most promising next incomplete row for returning any-k complete result tuples at any point during the query execution process.We report a thorough experimental evaluation over multiple relation extractors. Our experiments demonstrate that our query processor returns complete result tuples while processing only very few Web pages.

The interest in multivariate statistical process control (MSPC) has increased as the industrial processes have become more complex. This paper presents an industrial process involving a plastic part in which, due to the number of correlated variables, the inversion of the covariance matrix becomes impossible, and the classical MSPC cannot be used to identify physical aspects that explain the causes of variation or to increase the knowledge about the process behaviour. In order to solve this problem, a Multivariate Statistical Process Control based on Principal Component Analysis (MSPC-PCA) approach was used and an R code was developed to implement it according some commercial software used for this purpose, namely the ProMV (c) 2016 from ProSensus, Inc. (www.prosensus.ca). Based on used dataset, it was possible to illustrate the principles of MSPC-PCA. This work intends to illustrate the implementation of MSPC-PCA in R step by step, to help the user community of R to be able to perform it., FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2013)

Deadlock prevention policies are used to solve the deadlock problems of FMSs. The theory of regions is recognized as the unique method for obtaining maximally permissive (i.e., optimal) controllers in the existing literature. In this paper, the selective siphons and critical markings method [17] is merged in the new deadlock prevention policy. First of all, reachability graph is still needed. Second, CMTSIs are identified. Further, selective siphons and critical markings method is used to check if all dead/quasi-dead markings of CMTSIs are covered by these selective siphons. Furthermore, choose anyone CMTSI that belongs to a same selective siphon to be processed. Finally, controllers are therefore obtained. Experimental results indicate that the computational cost can be reduced again. Besides, it is the most efficient policy to obtain maximal permissive behavior of Petri net models.

The classification of color image that contain skin pixel is a challenging process. It is challenging due to certain factor such as brightness, background similar to human skin represent the obstacles thereof. The basic approach of skin detection is color based classification. The human skin image is the composition of RGB color. The data values of skin is calculated by randomly sampling RGB color values of different face images which have different age, race, genders sets. However, its performance has not really been high because of the high overlapped degree between “skin” and “non-skin” pixels. This paper describes the linear norm fuzzy based twin support vector machine (LN-FTSVM)) approach for discriminate skin and non-skin data values of skin dataset and to enhance the skin recognition performance. The concept of fuzzy is resolved the unclassified and overlapped data region problems. If no decision function is positive for a data set, this data set is classified into a class with the large membership value. By computational experiments shows that Experiments result shows that the accuracy is improved by linear norm fuzzy based TSVM (LN-FTSVM)) over the conventional methods.

In this paper, the generalized detector (GD) constructed on the basis of the generalized approach to signal processing in noise is employed by synthetic aperture radar (SAR) system to define a target location. The GD is proposed to solve the common problem of target detection at the very low signal-to-noise-ratio (SNR) when the conventional detection algorithms are not able to do it. Simulation results demonstrate a GD performance superiority in comparison with the search for undefined maritime objects (SUMO) detector one.

Reviews from players regarding different characteristics of an esports game are one of the worthiest sources for the developers to enhance their services or adjust operating strategy. However, little research has been conducted on detecting esports players’ favorite topics dealing with topic modeling. Thus, this paper aims to use a data mining approach to analyze community data in the games domain available on Steam. We collected more than 1.2 million English reviews from four esports games up to August 2020 on Steam. Our contributions in this paper are: (i) we manually build a dataset by filtering out high-quality esports reviews, (ii) we then infer and group reviews into 3 groups with 19 topics, and (iii) we add more contributions to finding the emerging opinions of esports players towards the different topics of esports reviews, which might benefit further research on understanding esports reviews.

This paper studies the dynamic time-varying formation control of multi-agent systems. Aiming at the unmanned vehicle systems, based on the static time-varying formation in literature [18], a multi-agent dynamic time-varying formation control based on consistency is put forward. Additionally, the sufficient conditions for the agreement and the steps for solving the control parameters are given. The simulation is conducted to prove the effectiveness and robustness of the theoretical results.