A Abstractive Summarization is the specific task in text generation whose popular approaches are based on the strength of Recurrent Neural Network. With the purpose to take advantages of Convolution Neural Network in ...
详细信息
ISBN:
(纸本)9781450365390
A Abstractive Summarization is the specific task in text generation whose popular approaches are based on the strength of Recurrent Neural Network. With the purpose to take advantages of Convolution Neural Network in text representation, we propose to combine these above networks in our encoder to capture both the global and local features from the input documents. Simultaneously, our model also integrates the reinforced mechanism with the novel reward function to get the closer direction between the learning and evaluating process. Through the experiments in CNN/Daily Mail, our models gains the significant results. Especially, in ROUGE-1 and ROUGE-L, it outperforms the previous works in this task with the expressive improvement (39.09% in ROUGE-L F1-score).
A large of existing dimensionality reduction methods are aimed at preserving some properties of data, which cannot take label information into account. With the aim of reduced low-dimensional coordinate is used as too...
详细信息
A large of existing dimensionality reduction methods are aimed at preserving some properties of data, which cannot take label information into account. With the aim of reduced low-dimensional coordinate is used as tool for timing judgment of model updating, the concentration information should be incorporated into dimensionality reduction procedure, which is presented and named as Visualized Correlation and Distance Preserving dimensionality reduction method. To address the difficulty of 2D coordinate and 1D label correlation computation, pairwise distance matrices in both the subspace and label space are computed and the strictly lower triangular parts of these matrices are extracted and vectorized in column-major order, resulting in two vectors so that correlation can be computed. Distance preservation term is included as sub-objectivefunction to ensure the low distance dissimilarity between high and low coordinates. To reduce structural loss caused by sequential dimensionality reduction method, the projection matrix is concatenated to vector then optimized to ensure projection vectors are optimized synchronously. PCA transformation is continued to adjust the reduced coordinates to better suited for visual judgment.
In modern-day manufacturing process, flexible manufacturing system (FMS) is used for efficient production of parts. For manufacturing of specific parts, parts should be processed in a specified sequence of operations....
详细信息
ISBN:
(纸本)9789811513077;9789811513060
In modern-day manufacturing process, flexible manufacturing system (FMS) is used for efficient production of parts. For manufacturing of specific parts, parts should be processed in a specified sequence of operations. It will be better to identify different possible sequence of operations on different machines and their cost implications in case of any machine failures. In this paper, a case study is considered in which three machines produce three different parts by doing different operations. Each machine can perform all the different operations to produce all the three parts. All the operations can be done in all the three machines, and the production timings and corresponding costs are varying from machine to machine. The sequence of operations for different parts is different. The combined objective function (COF) is formulated by considering the two objectives minimizing the total flow time and minimization of total tool cost with equal weightages. MATLAB Code is written for identifying all the possible sequences of operations, computed their total flow time and tool costs. Best sequences are identified when all machines are working;first machine fails, second machine fails and third machine fails based on COF values.
In this present work, an attempt is made to solve FMS scheduling problem by considering the multiobjective using Modified Cuckoos Search Algorithm. combined objective function (COF) is formulated by considering two ob...
详细信息
ISBN:
(纸本)9789811513077;9789811513060
In this present work, an attempt is made to solve FMS scheduling problem by considering the multiobjective using Modified Cuckoos Search Algorithm. combined objective function (COF) is formulated by considering two objectives with equal weightage i.e., minimizing the machine idle time and minimizing the penalty cost. The problem considered is 43 jobs need to be manufactured by processing on 16 machines is taken from literature. Matlab program is written to calculate COF value and for finding best sequence, COF value MCS algorithm is implemented. Best sequence and COF values obtained by MCS algorithm are compared with values obtained by other Algorithms like SPT, LPT, PSO, GA & CS. It is observed that sequence obtained by Modified Cuckoos Search Algorithm is giving better COF values.
This article revisits recently proposed methods to determine the kernel parameter and the number of latent components for identifying kernel principal component analysis (KPCA) and kernel partial least squares (KPLS) ...
详细信息
This article revisits recently proposed methods to determine the kernel parameter and the number of latent components for identifying kernel principal component analysis (KPCA) and kernel partial least squares (KPLS) models. A detailed analysis shows that existing work is neither optimal nor efficient in determining these important parameters and may lead to erroneous estimates. In addition to that, most methods are not designed to simultaneously estimate both parameters, i.e. they require one parameter to be predetermined. To address these practically important issues, the article introduces a cross-validatory framework to optimally determine both parameters. Application studies to a simulation example and a total of three experimental or industrial data sets confirm that the cross-validatory framework outperforms existing methods and yields optimal estimations for both parameters. In sharp contrast, existing work has the potential to substantially overestimate the number of latent components and to provide inadequate estimates for the kernel parameter.
To solve the problem of spectrum deviation brought about by existing AR parameter estimation methods in engineering application, this paper proposed an improved and optimized estimation method for parameters of the AR...
详细信息
ISBN:
(纸本)9781424458981
To solve the problem of spectrum deviation brought about by existing AR parameter estimation methods in engineering application, this paper proposed an improved and optimized estimation method for parameters of the AR model with constant parameters. The method was figured out as followed. Firstly, the initial estimation of AR parameters was derived according to LS criteria;Secondly, through the parametric spectrum estimation formula and the consecutive function's extreme theorem, the mathematical constraint formula was derived with which a combined objective function was constructed under the guidance of punishment function idea;Lastly, the Genetic Algorithm was adopted to optimize the LS estimation of AR parameters. The method proposed was used to estimate spectrum of the vertical vibration acceleration voltage data in turning condition. The results demonstrated that the method proposed overcome such problem as the spectral spectrum deviation, characteristics extraction was more precise and inhibited side-lobes well.
The multiobjective transportation problem is introduced and an efficient algorithm for its solution has been developed. The algorithm takes advantage of using optimal solutions of any objectivefunction as the basic f...
详细信息
The multiobjective transportation problem is introduced and an efficient algorithm for its solution has been developed. The algorithm takes advantage of using optimal solutions of any objectivefunction as the basic feasible solutions (BFSs) for the succeeding objective and reducing the set of solutions to most compromising solution to the combined objective function. To demonstrate the effectiveness of the algorithm, the problem involving four objectives has been considered. As a particular case, the two objective transportation problem "Optimizing Dead Mileage in Urban Bus Routes" solved by Sharma and Prakash (Journal of Transportation Engineering, ASCE, 1986) has been re-investigated, It has been noticed that our algorithm finds the same results obtained earlier, however, in a simpler way and may even be successfully applied for solving transportation problems with more than two objectives.
暂无评论