Rule extraction is a main goal for rough set theory. This paper mainly constructs a new algorithm (LBRM Algorithm) for rule extraction based on rough membership. The confidence principle is established based on rough ...
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Aiming at the deficiency of the current meridian diagnosis algorithms, SVM is applied to meridian diagnosis system. The system structure is described firstly, then the model selection of SVM is discussed in detail by ...
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Aiming at the deficiency of the current meridian diagnosis algorithms, SVM is applied to meridian diagnosis system. The system structure is described firstly, then the model selection of SVM is discussed in detail by taking chronic pharyngitis as an example: one-against-one method is used to realize multi-class;the problem of non-symmetrical samples of C-SVM is solved by giving positive and negative samples of different weights;a margin-based bound on generalization method is used to search parameters of the model. Finally, test results show that the classifier, which is realized and tested using vc++6.0, possess a very high recognition rate and can be applied to meridian diagnosis system.
Because of OpenMP programs shielding the underlying parallel execution and scheduling details,data races and deadlocks are tend to occur during program ***,this paper puts forward the modeling method of OpenMP program...
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Because of OpenMP programs shielding the underlying parallel execution and scheduling details,data races and deadlocks are tend to occur during program ***,this paper puts forward the modeling method of OpenMP programs based on Petri *** flow of programs are modeled according to the semantics of program control statements and directives of OpenMP programs;Data flow of programs are modeled by abstracting read and write operations related to shared *** two detection algorithms of data race and deadlock for OpenMP program are given based on the coverability tree of Petri ***,corresponding software tool is designed and implemented,and an OpenMP program example of the dining philosophers problem is analyzed to indicate the effectiveness of this method and tools.
Particle swarm optimization (PSO) is a swarm intelligence technique that optimizes a problem by iterative exploration and exploitation in the search space. However, PSO cannot achieve the preservation of population di...
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Particle swarm optimization (PSO) is a swarm intelligence technique that optimizes a problem by iterative exploration and exploitation in the search space. However, PSO cannot achieve the preservation of population diversity on solving multimodal optimization problems, and once the swarm falls into local convergence, it cannot jump out of the local trap. In order to solve this problem, this paper presents a fast restarting particle swarm optimization (FRPSO), which uses a novel restarting strategy based on a discrete finite-time particle swarm optimization (DFPSO). Taking advantage of frequently speeding up the swarm to converge along with a greater exploitation capability and then jumping out of the trap, this algorithm can preserve population diversity and provide a superior solution. The experiment performs on twenty-five benchmark functions which consists of single-model, multimodal and hybrid composition problems, the experimental result demonstrates that the performance of the proposed FRPSO algorithm is better than the other three representatives of the advanced PSO algorithm on most of these functions.
Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in ident...
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Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multi-label learning problem. We present and compare two multi-label learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multi-label learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.
In order to apply our high efficiency fibre-channel token-routing network(shortened as FC-TR network) to the field of materials simulation research, a new MPI parallel computing environment is proposed and designed, a...
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In order to apply our high efficiency fibre-channel token-routing network(shortened as FC-TR network) to the field of materials simulation research, a new MPI parallel computing environment is proposed and designed, and independently developed a parallel programming environment FC-TR-MPI based on FC-TR network. In FC-TR-MPI, a new method was applied to point-to-point communication that the network communications between processes in the same computing node were changed into memory operations;moreover, according to the underlying software and hardware features of FC-TR network, new algorithms were proposed to optimize the communication performance of some collective communications. Experimental results show that, compared with Sca MPI parallel programming environment, FC-TR-MPI has a higher parallel efficiency and speedup.
At present, most of the work has focused on Web services composition from the QoS side, little work being done on investigating how to implement service selection based on transactional and QoS requirements. In compli...
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Petri nets are widely used to model flexible manufacturing systems(FMSs) because they can help analyze the properties and synthesize deadlock-free supervisory controllers of *** system of Simple Sequential Processes w...
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ISBN:
(纸本)9781479947249
Petri nets are widely used to model flexible manufacturing systems(FMSs) because they can help analyze the properties and synthesize deadlock-free supervisory controllers of *** system of Simple Sequential Processes with Resources(WS3PR) is an important subclass of Petri nets that can well model many *** work first gives new algorithms to check liveness for a WS3 PR net via its subnet trees and *** the computation complexity for the proposed method is shown in this paper,to be polynomial under certain ***,sufficient conditions for deciding liveness of a WS3 PR are *** example is used to illustrate the results.
This study was designed to develop solutions for facial action human computer interaction on embedded devices. This paper presents a new facial detection algorithm using dimension reduction and regionalization computi...
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ISBN:
(纸本)9781467374439
This study was designed to develop solutions for facial action human computer interaction on embedded devices. This paper presents a new facial detection algorithm using dimension reduction and regionalization computing. Then, a dynamic action judgement algorithm based on simplified Facial Expression Coding system is proposed to describe human facial action. This paper also provide the facial action capture system ***, an accuracy report is presented to validate the usefulness of system.
Zero-shot learning (ZSL) aims to recognize novel classes without training samples through transferring knowledge from seen classes, based on the assumption that both the seen and unseen classes share a latent semantic...
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ISBN:
(纸本)9781728176055;9781728176062
Zero-shot learning (ZSL) aims to recognize novel classes without training samples through transferring knowledge from seen classes, based on the assumption that both the seen and unseen classes share a latent semantic space. Previous works either focus on directly learning various mapping functions between visual space and semantic space, or searching a latent common subspace to alleviate semantic gap between different modalities. However, few methods directly learn modality-invariant representations for ZSL. In this paper, we propose a Cross-Modal Representation Reconstruction (CM-RR) framework to bridge the semantic gap between visual features and semantic attributes, as well as introducing a novel regularizer for automatically feature selection. Moreover, an iterative optimization process based on the ALM (Augmented Lagrangian Method) algorithm with the alternating direction strategy is developed to solve the proposed formulation. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach, and even the performance surpasses some deep learning based methods.
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