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Rodríguez, Lisbeth; Li, Xiaoou; MejíaAlvarez, Pedro
2 Citations
Vertical partitioning is a well known technique to improve query response time in relational databases. This consists in dividing a table into a set of fragments of attributes according to the queries run against the table. In dynamic systems the queries tend to change with time, so it is needed a dynamic vertical partitioning technique which adapts the fragments according to the changes in query patterns in order to avoid long query response time. In this paper, we propose an active system for dynamic vertical partitioning of relational databases, called DYVEP (DYnamic VErtical Partitioning). DYVEP uses active rules to vertically fragment and refragment a database without intervention of a database administrator (DBA), maintaining an acceptable query response time even when the query patterns in the database suffer changes. Experiments with the TPCH benchmark demonstrate efficient query response time.
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By
Wang, Jiacun; Tepfenhart, Bill; Li, Xiaoou
A workflow describes the flow of jobs of a business process. Executing a workflow requires resources. In many situations, business processes are constrained by scarce resources. Therefore, it is important to understand workflow resource requirement. In our previous work, we introduced resource oriented workflow nets (ROWN) and based on ROWN, an efficient algorithm for the analysis of the maximum resource requirement of a workflow (maxRR) was developed [11]. The maxRR is the minimum amount of resources that support workflow execution along every possible path. On the hand, when there is a resource shortage, it is important to find the minimum resource requirement (minRR), which is the minimum amount of resources that support workflow execution along at least one path. In this paper, we present an approach to analyzing the minRR.
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By
Li, Kang; Peng, JianXun; Fei, Minrui; Li, Xiaoou; Yu, Wen
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This paper investigates the construction of a wide class of singlehidden layer neural networks (SLNNs) with or without tunable parameters in the hidden nodes. It is a challenging problem if both the parameter training and determination of network size are considered simultaneously. Two alternative network construction methods are considered in this paper. Firstly, the discrete construction of SLNNs is introduced. The main objective is to select a subset of hidden nodes from a pool of candidates with parameters fixed ‘a priori’. This is called discrete construction since there are no parameters in the hidden nodes that need to be trained. The second approach is called continuous construction as all the adjustable network parameters are trained on the whole parameter space along the network construction process. In the second approach, there is no need to generate a pool of candidates, and the network grows one by one with the adjustable parameters optimized. The main contribution of this paper is to show that the network construction can be done using the above two alternative approaches, and these two approaches can be integrated within a unified analytic framework, leading to potentially significantly improved model performance and/or computational efficiency.
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By
Toxqui, Rigoberto Toxqui; Yu, Wen; Li, Xiaoou
6 Citations
This paper considers the problem of PD control of overhead crane in the presence of uncertainty associated with crane dynamics. By using radial basis function neural networks, these uncertainties can be compensated effectively. This new neural control can resolve the two problems for overhead crane control: 1) decrease steadystate error of normal PD control. 2) guarantee stability via neural compensation. By Lyapunov method and inputtostate stability technique, we prove that these robust controllers with neural compensators are stable. Realtime experiments are presented to show the applicability of the approach presented in this paper.
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By
Li, Xiaoou; Medina Marń, Joselito; Chapa, Sergio V.
5 Citations
Active database systems have been developed for applications needing an automatic reaction in response to certain conditions being satisfied or certain event occurring. The desired behavior is expressed by ECArules (eventconditionaction rules). Generally, ECA rules and their execution are represented by rule language, for example, defining TRIGGERs in an active database. Then, database behavior prediction or analysis can be realized through other approaches such as algebraic approach, trigger graph methods, etc.. Therefore, in such active databases, rule representation and processing are separated. In this paper we propose a structural model which integrates rule representation and processing entirely, it is called Conditional Colored Petri Net (CCPN). CCPN can model both rules themselves and their complicated interacting relation in an graphical way. If the rule base of an active database is modeled by CCPN, then rule simulation can be done. An example is illustrated in the paper.
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By
Li, Xiaoou; Yu, Wen
By using differential neural networks, we present a novel robust adaptive controller for a class of unknown nonlinear systems. First, deadzone and projection techniques are applied to neural model, such that the identification error is bounded and the weights are different from zero. Then, a linearization controller is designed based on the neuro identifier. Since the approximation capability of the neural networks is limited, four kinds of compensators are addressed.
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By
Yu, Wen; Li, Xiaoou
3 Citations
In general, RBF neural network cannot match nonlinear systems exactly. Unmodeled dynamic leads parameters drift and even instability problem. According to system identification theory, robust modification terms must be included in order to guarantee Lyapunov stability. This paper suggests new learning laws for normal and adjustable RBF neural networks based on InputtoState Stability (ISS) approach. The new learning schemes employ a timevarying learning rate that is determined from inputoutput data and model structure. The calculation of the learning rate does not need any prior information such as estimation of the modeling error bounds.
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By
Li, Xiaoou; Li, Kang
RNA sequences detection is timeconsuming because of its huge data set size. Although SVM has been proved to be useful, normal SVM is not suitable for classification of large data sets because of its high training complexity. A twostage SVM classification approach is introduced for fast classifying large data sets. Experimental results on several RNA sequences detection demonstrate that the proposed approach is promising for such applications.
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By
Sandoval, Alejandro Cruz; Yu, Wen; Li, Xiaoou
Many physical systems contains fast and slow phenomenons. In this paper we propose a dynamic neural networks with different timescales to model the nonlinear system. Passivitybased approach is used to derive stability conditions for neural identifer. Several stability properties, such as passivity, asymptotic stability, inputtostate stability and bounded input bounded output stability, are guaranteed in certain senses. Numerical examples are also given to demonstrate the effectiveness of the theoretical results.
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By
Rodriguez, Lisbeth; Li, Xiaoou
1 Citations
Efficient retrieval of multimedia objects is a key factor for the success of distributed multimedia databases. One way to provide faster access to multimedia objects in these databases is using vertical partitioning. In this paper, we present a vertical partitioning algorithm for distributed multimedia databases (MAVP) that takes into account the size of the multimedia objects in order to generate an optimal vertical partitioning scheme. The objective function of MAVP minimizes the amount of access to irrelevant data and the transportation cost of the queries in distributed multimedia databases to achieve efficient retrieval of multimedia objects. A cost model for evaluating vertical partitioning schemes in distributed multimedia databases is developed. Experimental results clarify the validness of the proposed algorithm.
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By
Rodríguez, Lisbeth; Li, Xiaoou
1 Citations
Vertical partitioning is a design technique widely employed in relational databases to reduce the number of irrelevant attributes accessed by the queries. Currently, due to the popularity of multimedia applications on the Internet, the need of using partitioning techniques in multimedia databases has arisen in order to use their potential advantages with regard to query optimization. In multimedia databases, the attributes tend to be of very large multimedia objects. Therefore, the reduction in the number of accesses to irrelevant objects would imply a considerable cost saving in the query execution. Nevertheless, the use of vertical partitioning techniques in multimedia databases implies two problems: 1) most vertical partitioning algorithms only take into account alphanumeric data, and 2) the partitioning process is carried out in a static way. In order to address these problems, we propose an active system called DYMOND, which performs a dynamic vertical partitioning in multimedia databases to improve query performance. Experimental results on benchmark multimedia databases clarify the validness of our system.
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By
Yu, Wen; Li, Xiaoou; Gonzalez, Jesus
Deep recurrent neural networks (RNN), such as LSTM, have many advantages over forward networks. However, the LSTM training method, such as backward propagation through time (BPTT), is really slow.
In this paper, by separating the LSTM cell into forward and recurrent substructures, we propose a much simpler and faster training method than the BPTT. The deep LSTM is modified by combining the deep RNN with the multilayer perceptron (MLP). The simulation results show that our fast training method for LSTM is better than BPTT for LSTM.
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By
Ortiz, Floriberto; Yu, Wen; MorenoArmendariz, Marco; Li, Xiaoou
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Normal fuzzy CMAC neural network performs well because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. In this paper, we use recurrent technique to overcome these problems and propose a new CMAC neural network, named recurrent fuzzy CMAC (RFCMAC). Since the structure of RFCMAC is more complex, normal training methods are difficult to be applied. A new simple algorithm with a timevarying learning rate is proposed to assure the learning algorithm is stable.
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By
López Chau, Asdrúbal; Li, Xiaoou; Yu, Wen; Cervantes, Jair; MejíaÁlvarez, Pedro
Show all (5)
2 Citations
Border points are those instances located at the outer margin of dense clusters of samples. The detection is important in many areas such as data mining, image processing, robotics, geographic information systems and pattern recognition. In this paper we propose a novel method to detect border samples. The proposed method makes use of a discretization and works on partitions of the set of points. Then the border samples are detected by applying an algorithm similar to the presented in reference [8] on the sides of convex hulls. We apply the novel algorithm on classification task of data mining; experimental results show the effectiveness of our method.
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By
Yu, Wen; Li, Xiaoou; Irwin, George W.
This chapter proposes a novel antiswing control strategy for an overhead crane. The controller includes both position regulation and antiswing control. Since the crane model is not exactly known, fuzzy rules are used to compensate friction, gravity as well as the coupling between position and antiswing control. A highgain observer is introduced to estimate the joint velocities to realize PD control. Using a Lyapunov method and an inputtostate stability technique, the controller is proven to be robustly stable with bounded uncertainties, if the membership functions are changed by certain learning rules and the observer is fast enough. Realtime experiments are presented comparing this new stable antiswing PD control strategy with regular crane controllers.
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By
ChavarríaBáez, Lorena; Li, Xiaoou; PalmaOrozco, Rosaura
One of the most important steps in the validation of active rules is the generation of test cases. In this paper we introduce a way to estimate the total number of test cases needed to validate the rule base completely. Using this value it is possible to get an objective validation level of the rule base.
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By
Cervantes, Jair; Li, Xiaoou; Yu, Wen
15 Citations
Support vector machine (SVM) has been successfully applied to solve a large number of classification problems. Despite its good theoretic foundations and good capability of generalization, it is a big challenging task for the large data sets due to the training complexity, high memory requirements and slow convergence. In this paper, we present a new method, SVM classification based on fuzzy clustering. Before applying SVM we use fuzzy clustering, in this stage the optimal number of clusters are not needed in order to have less computational cost. We only need to partition the training data set briefly. The SVM classification is realized with the center of the groups. Then the declustering and SVM classification via reduced data are used. The proposed approach is scalable to large data sets with high classification accuracy and fast convergence speed. Empirical studies show that the proposed approach achieves good performance for large data sets.
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By
Cervantes, Jair; Huang, DeShuang; Li, Xiaoou; Yu, Wen
Show all (4)
This paper presents a method for classification of imbalanced splicesite classification problems, the proposed method consists of the generation of artificial instances that are incorporated to the dataset. Additionally, the method uses a genetic algorithm to introduce just instances that improve the performance. Experimental results show that the proposed algorithm obtains a better accuracy to detect splicesites than other implementations on skewed datasets.
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By
RodríguezMazahua, Lisbeth; AlorHernández, Giner; Li, Xiaoou; Cervantes, Jair; LópezChau, Asdrúbal
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Currently, vertical partitioning has been used in multimedia databases in order to take advantage of its potential benefits in query optimization. Nevertheless, most vertical partitioning algorithms are static; this means that they optimize a vertical partitioning scheme (VPS) according to a workload, but if this workload suffers changes, the VPS may be degraded, which would result in long query response time. This paper presents a set of active rules to perform dynamic vertical partitioning in multimedia databases. First of all, these rules collect all the information that a vertical partitioning algorithm needs as input. Then, they evaluate this information in order to know if the database has experienced enough changes to trigger a performance evaluator. In this case, if the performance of the database falls below a threshold previously calculated by the rules, the vertical partitioning algorithm is triggered, which gets a new VPS. Finally, the rules materialize the new VPS. Our active rule base is implemented in the system DYMOND, which is an active rulebased system for dynamic vertical partitioning of multimedia databases. DYMOND’s architecture and workflow are presented in this paper. Moreover, a case study is used to clarify and evaluate the functionality of our active rule base. Additionally, authors of this paper performed a qualitative evaluation with the aim of comparing and evaluating DYMOND’s functionality. The results showed that DYMOND improved query performance in multimedia databases.
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By
Tovar, Julio César; Yu, Wen; Li, Xiaoou
This paper describes a novel fuzzy rulebased modeling approach for some slow industrial processses. Structure identification is realized by clustering and support vector machines. When the process is slow, fuzzy rules can be obtained automatically. Parameters identification uses the techniques of fuzzy neural networks. A timevarying learning rate assures stability of the modeling error.
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By
Yu, Wen; Li, Xiaoou
40 Citations
Dynamic neural networks with different timescales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed networks are stable. In this paper, the passivitybased approach is used to derive stability conditions for dynamic neural networks with different timescales. Several stability properties, such as passivity, asymptotic stability, inputtostate stability and bounded input bounded output stability, are guaranteed in certain senses. A numerical example is also given to demonstrate the effectiveness of the theoretical results.
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By
Yu, Wen; Francisco, Panuncio Cruz; Li, Xiaoou
12 Citations
A levitation platform can be inserted into a patient’s abdominal cavity to perform diagnosis or a surgery procedure with a significantly reduced level of incision and an enhanced field of vision and operation. This paper proposes a new controller for the magnetic levitation, which connects the neural control (NC) and the slidingmode control (SMC) serially, while the other neural slidingmode controllers (NSMC) combine NC and SMC parallel. We call the new NSMC as twostage neural sliding control. It has less chattering during its discrete realization and ensures finitetime convergence. Realtime experiments for a prototype of magnetic levitation in minimal invasion surgery are presented. The comparisons with other regular controllers, such as PID, NC, SMC, and normal NSMC, are made by experiment.
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